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Castillo H, Hanna P, Sachs LM, Buisine N, Godoy F, Gilbert C, Aguilera F, Muñoz D, Boisvert C, Debiais-Thibaud M, Wan J, Spicuglia S, Marcellini S. Xenopus tropicalis osteoblast-specific open chromatin regions reveal promoters and enhancers involved in human skeletal phenotypes and shed light on early vertebrate evolution. Cells Dev 2024; 179:203924. [PMID: 38692409 DOI: 10.1016/j.cdev.2024.203924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 04/18/2024] [Accepted: 04/26/2024] [Indexed: 05/03/2024]
Abstract
While understanding the genetic underpinnings of osteogenesis has far-reaching implications for skeletal diseases and evolution, a comprehensive characterization of the osteoblastic regulatory landscape in non-mammalian vertebrates is still lacking. Here, we compared the ATAC-Seq profile of Xenopus tropicalis (Xt) osteoblasts to a variety of non mineralizing control tissues, and identified osteoblast-specific nucleosome free regions (NFRs) at 527 promoters and 6747 distal regions. Sequence analyses, Gene Ontology, RNA-Seq and ChIP-Seq against four key histone marks confirmed that the distal regions correspond to bona fide osteogenic transcriptional enhancers exhibiting a shared regulatory logic with mammals. We report 425 regulatory regions conserved with human and globally associated to skeletogenic genes. Of these, 35 regions have been shown to impact human skeletal phenotypes by GWAS, including one trps1 enhancer and the runx2 promoter, two genes which are respectively involved in trichorhinophalangeal syndrome type I and cleidocranial dysplasia. Intriguingly, 60 osteoblastic NFRs also align to the genome of the elephant shark, a species lacking osteoblasts and bone tissue. To tackle this paradox, we chose to focus on dlx5 because its conserved promoter, known to integrate regulatory inputs during mammalian osteogenesis, harbours an osteoblast-specific NFR in both frog and human. Hence, we show that dlx5 is expressed in Xt and elephant shark odontoblasts, supporting a common cellular and genetic origin of bone and dentine. Taken together, our work (i) unravels the Xt osteogenic regulatory landscape, (ii) illustrates how cross-species comparisons harvest data relevant to human biology and (iii) reveals that a set of genes including bnc2, dlx5, ebf3, mir199a, nfia, runx2 and zfhx4 drove the development of a primitive form of mineralized skeletal tissue deep in the vertebrate lineage.
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Affiliation(s)
- Héctor Castillo
- Group for the Study of Developmental Processes (GDeP), School of Biological Sciences, University of Concepción, Chile.
| | - Patricia Hanna
- Group for the Study of Developmental Processes (GDeP), School of Biological Sciences, University of Concepción, Chile
| | - Laurent M Sachs
- UMR7221, Physiologie Moléculaire et Adaptation, CNRS, MNHN, Paris Cedex 05, France
| | - Nicolas Buisine
- UMR7221, Physiologie Moléculaire et Adaptation, CNRS, MNHN, Paris Cedex 05, France
| | - Francisco Godoy
- Group for the Study of Developmental Processes (GDeP), School of Biological Sciences, University of Concepción, Chile
| | - Clément Gilbert
- Université Paris-Saclay, CNRS, IRD, UMR Évolution, Génomes, Comportement et Écologie, 12 route 128, 91190 Gif-sur-Yvette, France
| | - Felipe Aguilera
- Group for the Study of Developmental Processes (GDeP), School of Biological Sciences, University of Concepción, Chile
| | - David Muñoz
- Group for the Study of Developmental Processes (GDeP), School of Biological Sciences, University of Concepción, Chile
| | - Catherine Boisvert
- School of Molecular and Life Sciences, Curtin University, Perth, WA, Australia
| | - Mélanie Debiais-Thibaud
- Institut des Sciences de l'Evolution de Montpellier, ISEM, Univ Montpellier, CNRS, IRD, Montpellier, France
| | - Jing Wan
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labelisée LIGUE contre le Cancer, Marseille, France
| | - Salvatore Spicuglia
- Aix-Marseille University, INSERM, TAGC, UMR 1090, Marseille, France; Equipe Labelisée LIGUE contre le Cancer, Marseille, France
| | - Sylvain Marcellini
- Group for the Study of Developmental Processes (GDeP), School of Biological Sciences, University of Concepción, Chile.
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2
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Kussick E, Johansen N, Taskin N, Wynalda B, Martinez R, Groce EL, Reding M, Liang E, Shulga L, Huang C, Casper T, Clark M, Ho W, Gao Y, van Velthoven CTJ, Sobieski C, Ferrer R, Berg MR, Curtis BC, English C, Day JC, Fortuna M, Donadio N, Newman D, Yao S, Chakka AB, Goldy J, Torkelson A, Guzman JB, Chakrabarty R, Nguy B, Guilford N, Pham TH, Wright V, Ronellenfitch K, Gudsnuk K, Thyagarajan B, Smith KA, Dee N, Zeng H, Yao Z, Tasic B, Levi BP, Hodge R, Bakken TE, Lein ES, Ting JT, Daigle TL. Enhancer AAVs for targeting spinal motor neurons and descending motor pathways in rodents and macaque. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.30.605864. [PMID: 39131318 PMCID: PMC11312589 DOI: 10.1101/2024.07.30.605864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Experimental access to cell types within the mammalian spinal cord is severely limited by the availability of genetic tools. To enable access to lower motor neurons (LMNs) and LMN subtypes, which function to integrate information from the brain and control movement through direct innervation of effector muscles, we generated single cell multiome datasets from mouse and macaque spinal cords and discovered putative enhancers for each neuronal population. We cloned these enhancers into adeno-associated viral vectors (AAVs) driving a reporter fluorophore and functionally screened them in mouse. The most promising candidate enhancers were then extensively characterized using imaging and molecular techniques and further tested in rat and macaque to show conservation of LMN labeling. Additionally, we combined enhancer elements into a single vector to achieve simultaneous labeling of upper motor neurons (UMNs) and LMNs. This unprecedented LMN toolkit will enable future investigations of cell type function across species and potential therapeutic interventions for human neurodegenerative diseases.
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Affiliation(s)
- Emily Kussick
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Naz Taskin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brooke Wynalda
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Erin L Groce
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Melissa Reding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Cindy Huang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Clark
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Windy Ho
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yuan Gao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Rebecca Ferrer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Melissa R Berg
- Washington National Primate Research Center, Seattle, WA 98195 USA
| | - Britni C Curtis
- Washington National Primate Research Center, Seattle, WA 98195 USA
| | - Chris English
- Washington National Primate Research Center, Seattle, WA 98195 USA
| | - Jesse C Day
- Washington National Primate Research Center, Seattle, WA 98195 USA
| | - Michal Fortuna
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Dakota Newman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Beagen Nguy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Vonn Wright
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- University of Washington, Seattle, WA 98195 USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- University of Washington, Seattle, WA 98195 USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- University of Washington, Seattle, WA 98195 USA
| | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- University of Washington, Seattle, WA 98195 USA
- Lead Contact
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3
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Quan J, Yang M, Wang X, Cai G, Ding R, Zhuang Z, Zhou S, Tan S, Ruan D, Wu J, Zheng E, Zhang Z, Liu L, Meng F, Wu J, Xu C, Qiu Y, Wang S, Lin M, Li S, Ye Y, Zhou F, Lin D, Li X, Deng S, Zhang Y, Yao Z, Gao X, Yang Y, Liu Y, Zhan Y, Liu Z, Zhang J, Ma F, Yang J, Chen Q, Yang J, Ye J, Dong L, Gu T, Huang S, Xu Z, Li Z, Yang J, Huang W, Wu Z. Multi-omic characterization of allele-specific regulatory variation in hybrid pigs. Nat Commun 2024; 15:5587. [PMID: 38961076 PMCID: PMC11222378 DOI: 10.1038/s41467-024-49923-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 06/25/2024] [Indexed: 07/05/2024] Open
Abstract
Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.
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Affiliation(s)
- Jianping Quan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Zhongxin Breeding Technology Co., Ltd, Guangzhou, Guangdong, China
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, Guangdong, China
| | - Xingwang Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Gengyuan Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Rongrong Ding
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
- Guangdong Zhongxin Breeding Technology Co., Ltd, Guangzhou, Guangdong, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, Guangdong, China
| | - Zhanwei Zhuang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Shenping Zhou
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Suxu Tan
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Donglin Ruan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Jiajin Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Enqin Zheng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zebin Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Langqing Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Fanming Meng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of Animal Breeding and Nutrition, Guangzhou, Guangdong, China
| | - Jie Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Cineng Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yibin Qiu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Shiyuan Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Meng Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Shaoyun Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yong Ye
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Fuchen Zhou
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Danyang Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Xuehua Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Shaoxiong Deng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yuling Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zekai Yao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Xin Gao
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Yingshan Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yiyi Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yuexin Zhan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zhihong Liu
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Jiaming Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Fucai Ma
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Jifei Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Qiaoer Chen
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Jisheng Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Jian Ye
- Guangdong Zhongxin Breeding Technology Co., Ltd, Guangzhou, Guangdong, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, Guangdong, China
| | - Linsong Dong
- Guangdong Zhongxin Breeding Technology Co., Ltd, Guangzhou, Guangdong, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, Guangdong, China
| | - Ting Gu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Sixiu Huang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zheng Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zicong Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Jie Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China.
- State Regional Livestock and Poultry Genebank, Guangdong Genebank of Livestock and Poultry, South China Agricultural University, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China.
| | - Wen Huang
- Department of Animal Science, Michigan State University, East Lansing, MI, USA.
| | - Zhenfang Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China.
- Guangdong Zhongxin Breeding Technology Co., Ltd, Guangzhou, Guangdong, China.
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, Guangdong, China.
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4
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Xu J, Guo P, Hao S, Shangguan S, Shi Q, Volpe G, Huang K, Zuo J, An J, Yuan Y, Cheng M, Deng Q, Zhang X, Lai G, Nan H, Wu B, Shentu X, Wu L, Wei X, Jiang Y, Huang X, Pan F, Song Y, Li R, Wang Z, Liu C, Liu S, Li Y, Yang T, Xu Z, Du W, Li L, Ahmed T, You K, Dai Z, Li L, Qin B, Li Y, Lai L, Qin D, Chen J, Fan R, Li Y, Hou J, Ott M, Sharma AD, Cantz T, Schambach A, Kristiansen K, Hutchins AP, Göttgens B, Maxwell PH, Hui L, Xu X, Liu L, Chen A, Lai Y, Esteban MA. A spatiotemporal atlas of mouse liver homeostasis and regeneration. Nat Genet 2024; 56:953-969. [PMID: 38627598 DOI: 10.1038/s41588-024-01709-7] [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: 01/08/2023] [Accepted: 03/06/2024] [Indexed: 05/09/2024]
Abstract
The mechanism by which mammalian liver cell responses are coordinated during tissue homeostasis and perturbation is poorly understood, representing a major obstacle in our understanding of many diseases. This knowledge gap is caused by the difficulty involved with studying multiple cell types in different states and locations, particularly when these are transient. We have combined Stereo-seq (spatiotemporal enhanced resolution omics-sequencing) with single-cell transcriptomic profiling of 473,290 cells to generate a high-definition spatiotemporal atlas of mouse liver homeostasis and regeneration at the whole-lobe scale. Our integrative study dissects in detail the molecular gradients controlling liver cell function, systematically defining how gene networks are dynamically modulated through intercellular communication to promote regeneration. Among other important regulators, we identified the transcriptional cofactor TBL1XR1 as a rheostat linking inflammation to Wnt/β-catenin signaling for facilitating hepatocyte proliferation. Our data and analytical pipelines lay the foundation for future high-definition tissue-scale atlases of organ physiology and malfunction.
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Affiliation(s)
- Jiangshan Xu
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Pengcheng Guo
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, China.
- 3DC STAR, Spatiotemporal Campus at BGI Shenzhen, Shenzhen, China.
| | - Shijie Hao
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Shuncheng Shangguan
- BGI Research, Shenzhen, China
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, China
| | - Quan Shi
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Giacomo Volpe
- Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori 'Giovanni Paolo II', Bari, Italy
| | - Keke Huang
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Jing Zuo
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Juan An
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Yue Yuan
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Mengnan Cheng
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Qiuting Deng
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xiao Zhang
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, China
| | - Guangyao Lai
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Haitao Nan
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Baihua Wu
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Xinyi Shentu
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Liang Wu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Xiaoyu Wei
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Yujia Jiang
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Xin Huang
- BGI Research, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Fengyu Pan
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Yumo Song
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Ronghai Li
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Zhifeng Wang
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Chuanyu Liu
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China
| | - Shiping Liu
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | | | - Tao Yang
- China National GeneBank, BGI Research, Shenzhen, China
- Guangdong Provincial Genomics Data Center, BGI Research, Shenzhen, China
| | - Zhicheng Xu
- China National GeneBank, BGI Research, Shenzhen, China
- Guangdong Provincial Genomics Data Center, BGI Research, Shenzhen, China
| | - Wensi Du
- China National GeneBank, BGI Research, Shenzhen, China
- Guangdong Provincial Genomics Data Center, BGI Research, Shenzhen, China
| | - Ling Li
- China National GeneBank, BGI Research, Shenzhen, China
- Guangdong Provincial Genomics Data Center, BGI Research, Shenzhen, China
| | - Tanveer Ahmed
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Kai You
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Zhen Dai
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Li Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Baoming Qin
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Yinxiong Li
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Liangxue Lai
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, CAS Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Dajiang Qin
- The Fifth Affiliated Hospital of Guangzhou Medical University-BGI Research Center for Integrative Biology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Junling Chen
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, China
| | - Rong Fan
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, China
| | - Yongyin Li
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, China
| | - Jinlin Hou
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, China
| | - Michael Ott
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany
| | - Amar Deep Sharma
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany
| | - Tobias Cantz
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany
| | - Axel Schambach
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany
| | | | - Andrew P Hutchins
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Berthold Göttgens
- Department of Haematology and Wellcome MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Patrick H Maxwell
- Cambridge Institute for Medical Research, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Lijian Hui
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Xun Xu
- BGI Research, Hangzhou, China.
- BGI Research, Shenzhen, China.
- BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China.
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China.
| | - Longqi Liu
- BGI Research, Hangzhou, China.
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
- BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China.
| | - Ao Chen
- BGI Research, Shenzhen, China.
- Department of Biology, University of Copenhagen, Copenhagen, Denmark.
- BGI Research, Chongqing, China.
- JFL-BGI STOmics Center, BGI-Shenzhen, Chongqing, China.
| | - Yiwei Lai
- BGI Research, Hangzhou, China.
- BGI Research, Shenzhen, China.
- 3DC STAR, Spatiotemporal Campus at BGI Shenzhen, Shenzhen, China.
- BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan, China.
| | - Miguel A Esteban
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, China.
- 3DC STAR, Spatiotemporal Campus at BGI Shenzhen, Shenzhen, China.
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
- The Fifth Affiliated Hospital of Guangzhou Medical University-BGI Research Center for Integrative Biology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany.
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5
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Broman MT, Nadadur RD, Perez-Cervantes C, Burnicka-Turek O, Lazarevic S, Gams A, Laforest B, Steimle JD, Iddir S, Wang Z, Smith L, Mazurek SR, Olivey HE, Zhou P, Gadek M, Shen KM, Khan Z, Theisen JW, Yang XH, Ikegami K, Efimov IR, Pu WT, Weber CR, McNally EM, Svensson EC, Moskowitz IP. A Genomic Link From Heart Failure to Atrial Fibrillation Risk: FOG2 Modulates a TBX5/GATA4-Dependent Atrial Gene Regulatory Network. Circulation 2024; 149:1205-1230. [PMID: 38189150 PMCID: PMC11152454 DOI: 10.1161/circulationaha.123.066804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND The relationship between heart failure (HF) and atrial fibrillation (AF) is clear, with up to half of patients with HF progressing to AF. The pathophysiological basis of AF in the context of HF is presumed to result from atrial remodeling. Upregulation of the transcription factor FOG2 (friend of GATA2; encoded by ZFPM2) is observed in human ventricles during HF and causes HF in mice. METHODS FOG2 expression was assessed in human atria. The effect of adult-specific FOG2 overexpression in the mouse heart was evaluated by whole animal electrophysiology, in vivo organ electrophysiology, cellular electrophysiology, calcium flux, mouse genetic interactions, gene expression, and genomic function, including a novel approach for defining functional transcription factor interactions based on overlapping effects on enhancer noncoding transcription. RESULTS FOG2 is significantly upregulated in the human atria during HF. Adult cardiomyocyte-specific FOG2 overexpression in mice caused primary spontaneous AF before the development of HF or atrial remodeling. FOG2 overexpression generated arrhythmia substrate and trigger in cardiomyocytes, including calcium cycling defects. We found that FOG2 repressed atrial gene expression promoted by TBX5. FOG2 bound a subset of GATA4 and TBX5 co-bound genomic locations, defining a shared atrial gene regulatory network. FOG2 repressed TBX5-dependent transcription from a subset of co-bound enhancers, including a conserved enhancer at the Atp2a2 locus. Atrial rhythm abnormalities in mice caused by Tbx5 haploinsufficiency were rescued by Zfpm2 haploinsufficiency. CONCLUSIONS Transcriptional changes in the atria observed in human HF directly antagonize the atrial rhythm gene regulatory network, providing a genomic link between HF and AF risk independent of atrial remodeling.
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Affiliation(s)
- Michael T. Broman
- Department of Medicine, Section of Cardiology, University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637
| | - Rangarajan D. Nadadur
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Carlos Perez-Cervantes
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Ozanna Burnicka-Turek
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Sonja Lazarevic
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Anna Gams
- Department of Biomedical Engineering, George Washington University
| | - Brigitte Laforest
- Department of Medicine, Section of Cardiology, University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637
| | - Jeffrey D. Steimle
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Sabrina Iddir
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Zhezhen Wang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Linsin Smith
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Stefan R. Mazurek
- Department of Medicine, Section of Cardiology, University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637
| | - Harold E. Olivey
- Department of Biology, Indiana University Northwest, Gary, IN 46408
| | | | - Margaret Gadek
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Kaitlyn M. Shen
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Zoheb Khan
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Joshua W.M. Theisen
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Xinan H. Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
| | - Kohta Ikegami
- Division of Molecular and Cardiovascular Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229
| | - Igor R. Efimov
- Department of Biomedical Engineering, George Washington University
| | - William T. Pu
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, 02138
- Department of Cardiology, Boston Children’s Hospital, Boston, MA, 02115
| | | | - Elizabeth M. McNally
- Center for Genetic Medicine, Northwestern University, 303 E. Superior, SQ5-516, Chicago, IL 60611
| | | | - Ivan P. Moskowitz
- Department of Pediatrics, University of Chicago, Chicago, IL 60637
- Department of Pathology, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60637
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6
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Okwori M, Eslami A. Feature engineering from meta-data for prediction of differentially expressed genes: An investigation of Mus musculus exposed to space-conditions. Comput Biol Chem 2024; 109:108026. [PMID: 38335853 DOI: 10.1016/j.compbiolchem.2024.108026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/29/2023] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) when exposed to conditions in space from a set of diverse engineered features. To do this, we collected DEGs and non-differentially expressed genes (NDEGs) of Mus musculus-based experiments on the GeneLab database. We engineered a diverse set of features from factors reported in the literature to affect gene expression. An extreme gradient boosting (XGBoost) model was trained to predict if a given gene would be differentially expressed at various levels of differential expression. The test results on a separate holdout dataset showed an area under the receiver operating characteristics curves (AUCs) of 0.90±0.07, averaged across the five selected percentages of the most and least differentially expressed genes. Subsequently, we investigated the impact of selection of features, both individually with a correlation-based feature-selection procedure and in groups with a combination procedure, on the prediction performance. The feature selection confirmed some known drivers of adaptation to radiation and highlighted some new transcription factors and micro RNAs (miRNAs). Finally, gene ontology (GO) analysis revealed biological processes that tend to have expression patterns most suitable for this approach. This work highlights the potential of detection of differentially expressed genes using a machine learning (ML) approach, and provides some evidence of gene expression changes being captured by a diverse feature set not related to the condition under study.
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Affiliation(s)
- Michael Okwori
- Department of Electrical, Computer and Biomedical Engineering, Union College, Schenectady, 12308, NY, United States of America.
| | - Ali Eslami
- Department of Electrical and Computer Engineering, Wichita State University, Wichita, 67260, KS, United States of America
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7
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Zhang G, Fu Y, Yang L, Ye F, Zhang P, Zhang S, Ma L, Li J, Wu H, Han X, Wang J, Guo G. Construction of single-cell cross-species chromatin accessibility landscapes with combinatorial-hybridization-based ATAC-seq. Dev Cell 2024; 59:793-811.e8. [PMID: 38330939 DOI: 10.1016/j.devcel.2024.01.015] [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: 06/01/2023] [Revised: 11/03/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Despite recent advances in single-cell genomics, the lack of maps for single-cell candidate cis-regulatory elements (cCREs) in non-mammal species has limited our exploration of conserved regulatory programs across vertebrates and invertebrates. Here, we developed a combinatorial-hybridization-based method for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) named CH-ATAC-seq, enabling the construction of single-cell accessible chromatin landscapes for zebrafish, Drosophila, and earthworms (Eisenia andrei). By integrating scATAC censuses of humans, monkeys, and mice, we systematically identified 152 distinct main cell types and around 0.8 million cell-type-specific cCREs. Our analysis provided insights into the conservation of neural, muscle, and immune lineages across species, while epithelial cells exhibited a higher organ-origin heterogeneity. Additionally, a large-scale gene regulatory network (GRN) was constructed in four vertebrates by integrating scRNA-seq censuses. Overall, our study provides a valuable resource for comparative epigenomics, identifying the evolutionary conservation and divergence of gene regulation across different species.
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Affiliation(s)
- Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China
| | - Peijing Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Shuang Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Hanyu Wu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou 310058, China.
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China.
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou 310058, China; Institute of Hematology, Zhejiang University, Hangzhou, China.
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8
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Santhanagopalan I, Netzl A, Mathur T, Smith A, Griffiths H, Holzer A. Protocol to isolate nuclei from Chlamydomonas reinhardtii for ATAC sequencing. STAR Protoc 2024; 5:102764. [PMID: 38236771 PMCID: PMC10828896 DOI: 10.1016/j.xpro.2023.102764] [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: 07/13/2023] [Revised: 10/13/2023] [Accepted: 11/21/2023] [Indexed: 02/03/2024] Open
Abstract
The isolation of sufficient amounts of intact nuclei is essential to obtain high-resolution maps of chromatin accessibility via assay for transposase-accessible chromatin using sequencing (ATAC-seq). Here, we present a protocol for tag-free isolation of nuclei from both cell walled and cell wall-deficient strains of the green model alga Chlamydomonas reinhardtii at a suitable quality for ATAC-seq. We describe steps for nuclei isolation, quantification, and downstream ATAC-seq. This protocol is optimized to shorten the time of isolation and quantification of nuclei.
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Affiliation(s)
- Indu Santhanagopalan
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK.
| | - Antonia Netzl
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK; Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Tanya Mathur
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK; Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Alison Smith
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - Howard Griffiths
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - Andre Holzer
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK; Center for Bioinformatics and Department of Computer Science, Saarland University, 66123 Saarbrücken, Germany.
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9
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Cheng JH, Zheng C, Yamada R, Okada D. Visualization of the landscape of the read alignment shape of ATAC-seq data using Hellinger distance metric. Genes Cells 2024; 29:5-16. [PMID: 37989133 DOI: 10.1111/gtc.13082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/25/2023] [Accepted: 10/28/2023] [Indexed: 11/23/2023]
Abstract
Assay for Transposase-Accessible Chromatin using high-throughput sequencing (ATAC-seq) is the popular technique using next-generation sequencing to measure chromatin accessibility and identify open chromatin regions. While read alignment shape information of next-generation sequencing data with intensity information has been used in various bioinformatics methods, few studies have focused on pure shape information alone. In this study, we investigated what types of ATAC-seq read alignment shapes are observed for the promoter region and whether the pure shape information was related or unrelated to other gene features. We introduced a novel concept and pipeline for handling the pure shape information of NGS data as probability distributions and quantifying their dissimilarities by information theory. Based on this concept, we demonstrate that the pure shape information of ATAC-seq data is correlated with chromatin openness and some gene characteristics. On the other hand, it is suggested that the pure information of ATAC-seq read alignment shape is unlikely to contain additional information to explain differences in RNA expression. Our study suggests that viewing the read alignment shape of NGS data as probability distributions enables us to capture the characteristics of the genome-wide landscape of such data in a non-parametric manner.
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Affiliation(s)
- Jian Hao Cheng
- Center for Genomics Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Cheng Zheng
- Center for Genomics Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryo Yamada
- Center for Genomics Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Daigo Okada
- Center for Genomics Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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10
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Li J, Chen Z, Bai Y, Wei Y, Guo D, Liu Z, Niu Y, Shi B, Zhang X, Cai Y, Zhao Z, Hu J, Wang J, Liu X, Li S, Zhao F. Integration of ATAC-Seq and RNA-Seq Analysis to Identify Key Genes in the Longissimus Dorsi Muscle Development of the Tianzhu White Yak. Int J Mol Sci 2023; 25:158. [PMID: 38203329 PMCID: PMC10779322 DOI: 10.3390/ijms25010158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
During the postnatal stages, skeletal muscle development undergoes a series of meticulously regulated alterations in gene expression. However, limited studies have employed chromatin accessibility to unravel the underlying molecular mechanisms governing muscle development in yak species. Therefore, we conducted an analysis of both gene expression levels and chromatin accessibility to comprehensively characterize the dynamic genome-wide chromatin accessibility during muscle growth and development in the Tianzhu white yak, thereby elucidating the features of accessible chromatin regions throughout this process. Initially, we compared the differences in chromatin accessibility between two groups and observed that calves exhibited higher levels of chromatin accessibility compared to adult cattle, particularly within ±2 kb of the transcription start site (TSS). In order to investigate the correlation between alterations in chromatin accessible regions and variations in gene expression levels, we employed a combination of ATAC-seq and RNA-seq techniques, leading to the identification of 18 central transcriptional factors (TFs) and 110 key genes with significant effects. Through further analysis, we successfully identified several TFs, including Sp1, YY1, MyoG, MEF2A and MEF2C, as well as a number of candidate genes (ANKRD2, ANKRD1, BTG2 and LMOD3) which may be closely associated with muscle growth and development. Moreover, we constructed an interactive network program encompassing hub TFs and key genes related to muscle growth and development. This innovative approach provided valuable insights into the molecular mechanism underlying skeletal muscle development in the postnatal stages of Tianzhu white yaks while also establishing a solid theoretical foundation for future research on yak muscle development.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Zhidong Zhao
- Gansu Key Laboratory of Herbivorous Animal Biotechnology, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
| | - Jiang Hu
- Gansu Key Laboratory of Herbivorous Animal Biotechnology, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
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11
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Han W, Zhang J, Li M, An M, Li L. Analysis of Chromatin Accessibility Changes Induced by BMMC Recognition of Foot-and-Mouth Disease Virus-like Particles through ATAC-seq. Int J Mol Sci 2023; 24:17044. [PMID: 38069369 PMCID: PMC10706935 DOI: 10.3390/ijms242317044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
Mast cells can recognize foot-and-mouth disease virus-like particles (FMDV-VLPs) via mannose receptors (MRs) to produce differentially expressed cytokines. The regulatory role of chromatin accessibility in this process is unclear. Bone marrow-derived mast cells (BMMCs) were cultured, and an assay of transposase-accessible chromatin sequencing (ATAC-seq) was applied to demonstrate the regulation of chromatin accessibility in response to the BMMCs' recognition of FMDV-VLPs. A pathway enrichment analysis showed that peaks associated with the nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3 kinase-protein kinase B (PI3K-Akt), and other signaling pathways, especially the NF-κB pathway, were involved in the BMMCs' recognition of VLPs. Moreover, transcription factors including SP1, NRF1, AP1, GATA3, microphthalmia-associated transcription factor (MITF), and NF-κB-p65 may bind to the motifs with altered chromatin accessibility to regulate gene transcription. Furthermore, the expression of NF-κB, interleukin (IL)-9, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ in the BMMCs of the VLP group increased compared with that of the BMMCs in the control group, whereas the expression of IL-10 did not differ significantly between groups. After inhibiting the MRs, the expression of NF-κB, IL-9, TNF-α, and IFN-γ decreased significantly, whereas the expression of IL-10 increased. The expression of MAPK and IL-6 showed no significant change after MR inhibition. This study demonstrated that MRs expressed on BMMCs can affect the NF-κB pathway by changing chromatin accessibility to regulate the transcription of specific cytokines, ultimately leading to the differential expression of cytokines. These data provide a theoretical basis and new ideas for the development of a novel vaccine for FMD.
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Affiliation(s)
| | | | | | | | - Limin Li
- College of Veterinary Medicine, Hebei Agricultural University, Baoding 071000, China; (W.H.); (J.Z.); (M.L.); (M.A.)
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12
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Abatti LE, Lado-Fernández P, Huynh L, Collado M, Hoffman M, Mitchell J. Epigenetic reprogramming of a distal developmental enhancer cluster drives SOX2 overexpression in breast and lung adenocarcinoma. Nucleic Acids Res 2023; 51:10109-10131. [PMID: 37738673 PMCID: PMC10602899 DOI: 10.1093/nar/gkad734] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/18/2023] [Accepted: 08/24/2023] [Indexed: 09/24/2023] Open
Abstract
Enhancer reprogramming has been proposed as a key source of transcriptional dysregulation during tumorigenesis, but the molecular mechanisms underlying this process remain unclear. Here, we identify an enhancer cluster required for normal development that is aberrantly activated in breast and lung adenocarcinoma. Deletion of the SRR124-134 cluster disrupts expression of the SOX2 oncogene, dysregulates genome-wide transcription and chromatin accessibility and reduces the ability of cancer cells to form colonies in vitro. Analysis of primary tumors reveals a correlation between chromatin accessibility at this cluster and SOX2 overexpression in breast and lung cancer patients. We demonstrate that FOXA1 is an activator and NFIB is a repressor of SRR124-134 activity and SOX2 transcription in cancer cells, revealing a co-opting of the regulatory mechanisms involved in early development. Notably, we show that the conserved SRR124 and SRR134 regions are essential during mouse development, where homozygous deletion results in the lethal failure of esophageal-tracheal separation. These findings provide insights into how developmental enhancers can be reprogrammed during tumorigenesis and underscore the importance of understanding enhancer dynamics during development and disease.
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Affiliation(s)
- Luis E Abatti
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Patricia Lado-Fernández
- Laboratory of Cell Senescence, Cancer and Aging, Health Research Institute of Santiago de Compostela (IDIS), Xerencia de Xestión Integrada de Santiago (XXIS/SERGAS), Santiago de Compostela, Spain
- Department of Physiology and Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Linh Huynh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Manuel Collado
- Laboratory of Cell Senescence, Cancer and Aging, Health Research Institute of Santiago de Compostela (IDIS), Xerencia de Xestión Integrada de Santiago (XXIS/SERGAS), Santiago de Compostela, Spain
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Jennifer A Mitchell
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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13
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Gonçalves TM, Stewart CL, Baxley SD, Xu J, Li D, Gabel HW, Wang T, Avraham O, Zhao G. Towards a comprehensive regulatory map of Mammalian Genomes. RESEARCH SQUARE 2023:rs.3.rs-3294408. [PMID: 37841836 PMCID: PMC10571623 DOI: 10.21203/rs.3.rs-3294408/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Genome mapping studies have generated a nearly complete collection of genes for the human genome, but we still lack an equivalently vetted inventory of human regulatory sequences. Cis-regulatory modules (CRMs) play important roles in controlling when, where, and how much a gene is expressed. We developed a training data-free CRM-prediction algorithm, the Mammalian Regulatory MOdule Detector (MrMOD) for accurate CRM prediction in mammalian genomes. MrMOD provides genome position-fixed CRM models similar to the fixed gene models for the mouse and human genomes using only genomic sequences as the inputs with one adjustable parameter - the significance p-value. Importantly, MrMOD predicts a comprehensive set of high-resolution CRMs in the mouse and human genomes including all types of regulatory modules not limited to any tissue, cell type, developmental stage, or condition. We computationally validated MrMOD predictions used a compendium of 21 orthogonal experimental data sets including thousands of experimentally defined CRMs and millions of putative regulatory elements derived from hundreds of different tissues, cell types, and stimulus conditions obtained from multiple databases. In ovo transgenic reporter assay demonstrates the power of our prediction in guiding experimental design. We analyzed CRMs located in the chromosome 17 using unsupervised machine learning and identified groups of CRMs with multiple lines of evidence supporting their functionality, linking CRMs with upstream binding transcription factors and downstream target genes. Our work provides a comprehensive base pair resolution annotation of the functional regulatory elements and non-functional regions in the mammalian genomes.
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Affiliation(s)
| | | | | | - Jason Xu
- Missouri University of Science & Technology
| | - Daofeng Li
- Washington University School of Medicine
| | | | - Ting Wang
- Washington University School of Medicine
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14
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Bessonneau-Gaborit V, Cruard J, Guerin-Charbonnel C, Derrien J, Alberge JB, Douillard E, Devic M, Deshayes S, Campion L, Westermann F, Moreau P, Herrmann C, Bourdon J, Magrangeas F, Minvielle S. Exploring the impact of dexamethasone on gene regulation in myeloma cells. Life Sci Alliance 2023; 6:e202302195. [PMID: 37524526 PMCID: PMC10390781 DOI: 10.26508/lsa.202302195] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 08/02/2023] Open
Abstract
Among glucocorticoids (GCs), dexamethasone (Dex) is widely used in treatment of multiple myelomas. However, despite a definite benefit, all patients relapse. Moreover, the molecular basis of glucocorticoid efficacy remains elusive. To determine genomic response to Dex in myeloma cells, we generated bulk and single-cell multi-omics data and high-resolution contact maps of active enhancers and target genes. We show that a minority of glucocorticoid receptor-binding sites are associated with enhancer activity gains, increased interaction loops, and transcriptional activity. We identified and characterized a predominant enhancer enriched in cohesin (RAD21) and more accessible upon Dex exposure. Analysis of four gene-specific networks revealed the importance of the CTCF-cohesin couple and the synchronization of regulatory sequence openings for efficient transcription in response to Dex. Notably, these epigenomic changes are associated with cell-to-cell transcriptional heterogeneity, in particular, lineage-specific genes. As consequences, BCL2L11-encoding BIM critical for Dex-induced apoptosis and CXCR4 protective from chemotherapy-induced apoptosis are rather up-regulated in different cells. In summary, our work provides new insights into the molecular mechanisms involved in Dex escape.
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Affiliation(s)
- Victor Bessonneau-Gaborit
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Jonathan Cruard
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
| | - Catherine Guerin-Charbonnel
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Institut de Cancérologie de l'Ouest, Nantes, France
| | - Jennifer Derrien
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
| | - Jean-Baptiste Alberge
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
| | - Elise Douillard
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Magali Devic
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Sophie Deshayes
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
| | - Loïc Campion
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Institut de Cancérologie de l'Ouest, Nantes, France
| | - Frank Westermann
- Hopp Children's Cancer Center Heidelberg, KITZ, Heidelberg, Germany
- Division of Neuroblastoma Genomics, German Cancer Research Center, Heidelberg, Germany
| | - Phillipe Moreau
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Carl Herrmann
- Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg, Germany
| | | | - Florence Magrangeas
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Stéphane Minvielle
- Université de Nantes, CNRS, INSERM, Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, France
- Centre Hospitalier Universitaire, Nantes, France
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15
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Powell J, Talenti A, Fisch A, Hemmink JD, Paxton E, Toye P, Santos I, Ferreira BR, Connelley TK, Morrison LJ, Prendergast JGD. Profiling the immune epigenome across global cattle breeds. Genome Biol 2023; 24:127. [PMID: 37218021 DOI: 10.1186/s13059-023-02964-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 05/08/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Understanding the variation between well and poorly adapted cattle breeds to local environments and pathogens is essential for breeding cattle with improved climate and disease-resistant phenotypes. Although considerable progress has been made towards identifying genetic differences between breeds, variation at the epigenetic and chromatin levels remains poorly characterized. Here, we generate, sequence and analyse over 150 libraries at base-pair resolution to explore the dynamics of DNA methylation and chromatin accessibility of the bovine immune system across three distinct cattle lineages. RESULTS We find extensive epigenetic divergence between the taurine and indicine cattle breeds across immune cell types, which is linked to the levels of local DNA sequence divergence between the two cattle sub-species. The unique cell type profiles enable the deconvolution of complex cellular mixtures using digital cytometry approaches. Finally, we show distinct sub-categories of CpG islands based on their chromatin and methylation profiles that discriminate between classes of distal and gene proximal islands linked to discrete transcriptional states. CONCLUSIONS Our study provides a comprehensive resource of DNA methylation, chromatin accessibility and RNA expression profiles of three diverse cattle populations. The findings have important implications, from understanding how genetic editing across breeds, and consequently regulatory backgrounds, may have distinct impacts to designing effective cattle epigenome-wide association studies in non-European breeds.
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Affiliation(s)
- Jessica Powell
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK.
| | - Andrea Talenti
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK
| | - Andressa Fisch
- Ribeirão Preto College of Nursing, University of Sao Paulo, Ribeirão Preto, Brazil
| | - Johanneke D Hemmink
- Centre for Tropical Livestock Genetics and Health, Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK
- The International Livestock Research Institute, PO Box 30709, Nairobi, 00100, Kenya
| | - Edith Paxton
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK
| | - Philip Toye
- The International Livestock Research Institute, PO Box 30709, Nairobi, 00100, Kenya
- Centre for Tropical Livestock Genetics and Health, ILRI Kenya, PO Box 30709, Nairobi, 00100, Kenya
| | - Isabel Santos
- Ribeirão Preto College of Nursing, University of Sao Paulo, Ribeirão Preto, Brazil
| | - Beatriz R Ferreira
- Ribeirão Preto College of Nursing, University of Sao Paulo, Ribeirão Preto, Brazil
| | - Tim K Connelley
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK
- Centre for Tropical Livestock Genetics and Health, Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK
| | - Liam J Morrison
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK.
- Centre for Tropical Livestock Genetics and Health, Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK.
| | - James G D Prendergast
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK.
- Centre for Tropical Livestock Genetics and Health, Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, UK.
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16
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Kaplow IM, Lawler AJ, Schäffer DE, Srinivasan C, Sestili HH, Wirthlin ME, Phan BN, Prasad K, Brown AR, Zhang X, Foley K, Genereux DP, Karlsson EK, Lindblad-Toh K, Meyer WK, Pfenning AR. Relating enhancer genetic variation across mammals to complex phenotypes using machine learning. Science 2023; 380:eabm7993. [PMID: 37104615 PMCID: PMC10322212 DOI: 10.1126/science.abm7993] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 02/23/2023] [Indexed: 04/29/2023]
Abstract
Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species' phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer-phenotype associations, including brain size-associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.
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Affiliation(s)
- Irene M. Kaplow
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alyssa J. Lawler
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Daniel E. Schäffer
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Chaitanya Srinivasan
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Heather H. Sestili
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Morgan E. Wirthlin
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - BaDoi N. Phan
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Medical Scientist Training Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kavya Prasad
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ashley R. Brown
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xiaomeng Zhang
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Kathleen Foley
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Diane P. Genereux
- Broad Institute, Cambridge, MA, USA
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Elinor K. Karlsson
- Broad Institute, Cambridge, MA, USA
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Kerstin Lindblad-Toh
- Broad Institute, Cambridge, MA, USA
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Wynn K. Meyer
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Andreas R. Pfenning
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biology, Carnegie Mellon University, Pittsburgh, PA, USA
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17
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Peng S, Dahlgren AR, Donnelly CG, Hales EN, Petersen JL, Bellone RR, Kalbfleisch T, Finno CJ. Functional annotation of the animal genomes: An integrated annotation resource for the horse. PLoS Genet 2023; 19:e1010468. [PMID: 36862752 PMCID: PMC10013926 DOI: 10.1371/journal.pgen.1010468] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/14/2023] [Accepted: 01/28/2023] [Indexed: 03/03/2023] Open
Abstract
The genomic sequence of the horse has been available since 2009, providing critical resources for discovering important genomic variants regarding both animal health and population structures. However, to fully understand the functional implications of these variants, detailed annotation of the horse genome is required. Due to the limited availability of functional data for the equine genome, as well as the technical limitations of short-read RNA-seq, existing annotation of the equine genome contains limited information about important aspects of gene regulation, such as alternate isoforms and regulatory elements, which are either not transcribed or transcribed at a very low level. To solve above problems, the Functional Annotation of the Animal Genomes (FAANG) project proposed a systemic approach to tissue collection, phenotyping, and data generation, adopting the blueprint laid out by the Encyclopedia of DNA Elements (ENCODE) project. Here we detail the first comprehensive overview of gene expression and regulation in the horse, presenting 39,625 novel transcripts, 84,613 candidate cis-regulatory elements (CRE) and their target genes, 332,115 open chromatin regions genome wide across a diverse set of tissues. We showed substantial concordance between chromatin accessibility, chromatin states in different genic features and gene expression. This comprehensive and expanded set of genomics resources will provide the equine research community ample opportunities for studies of complex traits in the horse.
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Affiliation(s)
- Sichong Peng
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America
| | - Anna R. Dahlgren
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America
| | - Callum G. Donnelly
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America
| | - Erin N. Hales
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America
| | - Jessica L. Petersen
- Department of Animal Science, University of Nebraska—Lincoln, Lincoln, Nebraska, United States of America
| | - Rebecca R. Bellone
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America
- Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America
| | - Ted Kalbfleisch
- Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, United States of America
| | - Carrie J. Finno
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America
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18
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Chin HG, Vishnu US, Sun Z, Ponnaluri VKC, Zhang G, Xu SY, Benoukraf T, Cejas P, Spracklin G, Estève PO, Long HW, Pradhan S. Universal NicE-Seq: A Simple and Quick Method for Accessible Chromatin Detection in Fixed Cells. Methods Mol Biol 2023; 2611:39-52. [PMID: 36807062 DOI: 10.1007/978-1-0716-2899-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Genome-wide accessible chromatin sequencing and identification has enabled deciphering the epigenetic information encoded in chromatin, revealing accessible promoters, enhancers, nucleosome positioning, transcription factor occupancy, and other chromosomal protein binding. The starting biological materials are often fixed using formaldehyde crosslinking. Here, we describe accessible chromatin library preparation from low numbers of formaldehyde-crosslinked cells using a modified nick translation method, where a nicking enzyme nicks one strand of DNA and DNA polymerase incorporates biotin-conjugated dATP, dCTP, and methyl-dCTP. Once the DNA is labeled, it can be isolated for NGS library preparation. We termed this method as universal NicE-seq (nicking enzyme-assisted sequencing). We also demonstrate a single tube method that enables direct NGS library preparation from low cell numbers without DNA purification. Furthermore, we demonstrated universal NicE-seq on FFPE tissue section sample.
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Affiliation(s)
| | | | - Zhiyi Sun
- New England Biolabs Inc., Ipswich, MA, USA
| | | | | | | | - Touati Benoukraf
- Faculty of Medicine, Craig L. Dobbin Genetics Research Centre, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Paloma Cejas
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - George Spracklin
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Henry W Long
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
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19
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Kamimoto K, Stringa B, Hoffmann CM, Jindal K, Solnica-Krezel L, Morris SA. Dissecting cell identity via network inference and in silico gene perturbation. Nature 2023; 614:742-751. [PMID: 36755098 PMCID: PMC9946838 DOI: 10.1038/s41586-022-05688-9] [Citation(s) in RCA: 144] [Impact Index Per Article: 144.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 12/28/2022] [Indexed: 02/10/2023]
Abstract
Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.
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Affiliation(s)
- Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Blerta Stringa
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Christy M Hoffmann
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Kunal Jindal
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Lilianna Solnica-Krezel
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA.
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20
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Kawaguchi RK, Tang Z, Fischer S, Rajesh C, Tripathy R, Koo PK, Gillis J. Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. Brief Bioinform 2023; 24:bbac541. [PMID: 36549922 PMCID: PMC9851328 DOI: 10.1093/bib/bbac541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/29/2022] [Accepted: 11/08/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. RESULTS In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.
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Affiliation(s)
| | - Ziqi Tang
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Chandana Rajesh
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Rohit Tripathy
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Peter K Koo
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
- Department of Physiology and Donnelly Centre for Cellular & Biomolecular Research Department, University of Toronto, Ontario M5S 3E1, Canada
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21
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Zhu XN, Wang YZ, Li C, Wu HY, Zhang R, Hu XX, Zhang R, Hu XX. Chicken chromatin accessibility atlas accelerates epigenetic annotation of birds and gene fine-mapping associated with growth traits. Zool Res 2023; 44:53-62. [PMID: 36317479 PMCID: PMC9841184 DOI: 10.24272/j.issn.2095-8137.2022.228] [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] [Indexed: 01/04/2023] Open
Abstract
The development of epigenetic maps, such as the ENCODE project in humans, provides resources for gene regulation studies and a reference for research of disease-related regulatory elements. However, epigenetic information, such as a bird-specific chromatin accessibility atlas, is currently lacking for the thousands of bird species currently described. The major genomic difference between birds and mammals is their shorter introns and intergenic distances, which seriously hinders the use of humans and mice as a reference for studying the function of important regulatory regions in birds. In this study, using chicken as a model bird species, we systematically compiled a chicken chromatin accessibility atlas using 53 Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) samples across 11 tissues. An average of 50 796 open chromatin regions were identified per sample, cumulatively accounting for 20.36% of the chicken genome. Tissue specificity was largely reflected by differences in intergenic and intronic peaks, with specific functional regulation achieved by two mechanisms: recruitment of several sequence-specific transcription factors and direct regulation of adjacent functional genes. By integrating data from genome-wide association studies, our results suggest that chicken body weight is driven by different regulatory variants active in growth-relevant tissues. We propose CAB39L (active in the duodenum), RCBTB1 (muscle and liver), and novel long non-coding RNA ENSGALG00000053256 (bone) as candidate genes regulating chicken body weight. Overall, this study demonstrates the value of epigenetic data in fine-mapping functional variants and provides a compendium of resources for further research on the epigenetics and evolution of birds and mammals.
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Affiliation(s)
- Xiao-Ning Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yu-Zhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China,E-mail:
| | - Chong Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Han-Yu Wu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China
| | - Ran Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xiao-Xiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China,National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China,
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22
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Van den Berge K, Chou HJ, Roux de Bézieux H, Street K, Risso D, Ngai J, Dudoit S. Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects. CELL REPORTS METHODS 2022; 2:100321. [PMID: 36452861 PMCID: PMC9701614 DOI: 10.1016/j.crmeth.2022.100321] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 02/23/2022] [Accepted: 10/06/2022] [Indexed: 06/17/2023]
Abstract
The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.
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Affiliation(s)
- Koen Van den Berge
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Hsin-Jung Chou
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Hector Roux de Bézieux
- Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Kelly Street
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Davide Risso
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - John Ngai
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Sandrine Dudoit
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
- Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
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23
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Beucher A, Miguel-Escalada I, Balboa D, De Vas MG, Maestro MA, Garcia-Hurtado J, Bernal A, Gonzalez-Franco R, Vargiu P, Heyn H, Ravassard P, Ortega S, Ferrer J. The HASTER lncRNA promoter is a cis-acting transcriptional stabilizer of HNF1A. Nat Cell Biol 2022; 24:1528-1540. [PMID: 36202974 PMCID: PMC9586874 DOI: 10.1038/s41556-022-00996-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 08/16/2022] [Indexed: 11/08/2022]
Abstract
The biological purpose of long non-coding RNAs (lncRNAs) is poorly understood. Haploinsufficient mutations in HNF1A homeobox A (HNF1A), encoding a homeodomain transcription factor, cause diabetes mellitus. Here, we examine HASTER, the promoter of an lncRNA antisense to HNF1A. Using mouse and human models, we show that HASTER maintains cell-specific physiological HNF1A concentrations through positive and negative feedback loops. Pancreatic β cells from Haster mutant mice consequently showed variegated HNF1A silencing or overexpression, resulting in hyperglycaemia. HASTER-dependent negative feedback was essential to prevent HNF1A binding to inappropriate genomic regions. We demonstrate that the HASTER promoter DNA, rather than the lncRNA, modulates HNF1A promoter-enhancer interactions in cis and thereby regulates HNF1A transcription. Our studies expose a cis-regulatory element that is unlike classic enhancers or silencers, it stabilizes the transcription of its target gene and ensures the fidelity of a cell-specific transcription factor program. They also show that disruption of a mammalian lncRNA promoter can cause diabetes mellitus.
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Affiliation(s)
- Anthony Beucher
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Irene Miguel-Escalada
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Spain
| | - Diego Balboa
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Spain
| | - Matías G De Vas
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Miguel Angel Maestro
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Spain
| | - Javier Garcia-Hurtado
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Spain
| | - Aina Bernal
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Spain
| | - Roser Gonzalez-Franco
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Holger Heyn
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Philippe Ravassard
- Biotechnology and Biotherapy Team, Institut du Cerveau et de la Moelle, CNRS UMR7225, INSERM U975, University Pierre et Marie Curie, Paris, France
| | - Sagrario Ortega
- Transgenics Unit, Spanish National Cancer Research Centre, Madrid, Spain
| | - Jorge Ferrer
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Spain.
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24
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Hu K, Liu H, Lawson ND, Zhu LJ. scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data. Front Cell Dev Biol 2022; 10:981859. [PMID: 36238687 PMCID: PMC9551270 DOI: 10.3389/fcell.2022.981859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.
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Affiliation(s)
- Kai Hu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Haibo Liu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Nathan D. Lawson
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Lihua Julie Zhu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Program in Molecular Medicine, Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
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25
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Liu Y, Fu Y, Yang Y, Yi G, Lian J, Xie B, Yao Y, Chen M, Niu Y, Liu L, Wang L, Zhang Y, Fan X, Tang Y, Yuan P, Zhu M, Li Q, Zhang S, Chen Y, Wang B, He J, Lu D, Liachko I, Sullivan ST, Pang B, Chen Y, He X, Li K, Tang Z. Integration of multi-omics data reveals cis-regulatory variants that are associated with phenotypic differentiation of eastern from western pigs. GENETICS SELECTION EVOLUTION 2022; 54:62. [PMID: 36104777 PMCID: PMC9476355 DOI: 10.1186/s12711-022-00754-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
The genetic mechanisms that underlie phenotypic differentiation in breeding animals have important implications in evolutionary biology and agriculture. However, the contribution of cis-regulatory variants to pig phenotypes is poorly understood. Therefore, our aim was to elucidate the molecular mechanisms by which non-coding variants cause phenotypic differences in pigs by combining evolutionary biology analyses and functional genomics.
Results
We obtained a high-resolution phased chromosome-scale reference genome with a contig N50 of 18.03 Mb for the Luchuan pig breed (a representative eastern breed) and profiled potential selective sweeps in eastern and western pigs by resequencing the genomes of 234 pigs. Multi-tissue transcriptome and chromatin accessibility analyses of these regions suggest that tissue-specific selection pressure is mediated by promoters and distal cis-regulatory elements. Promoter variants that are associated with increased expression of the lysozyme (LYZ) gene in the small intestine might enhance the immunity of the gastrointestinal tract and roughage tolerance in pigs. In skeletal muscle, an enhancer-modulating single-nucleotide polymorphism that is associated with up-regulation of the expression of the troponin C1, slow skeletal and cardiac type (TNNC1) gene might increase the proportion of slow muscle fibers and affect meat quality.
Conclusions
Our work sheds light on the molecular mechanisms by which non-coding variants shape phenotypic differences in pigs and provides valuable resources and novel perspectives to dissect the role of gene regulatory evolution in animal domestication and breeding.
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26
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Wang J, Li B, Yang X, Liang C, Raza SHA, Pan Y, Zhang K, Zan L. Integration of RNA-seq and ATAC-seq identifies muscle-regulated hub genes in cattle. Front Vet Sci 2022; 9:925590. [PMID: 36032309 PMCID: PMC9404375 DOI: 10.3389/fvets.2022.925590] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
As the main product of livestock, muscle itself plays an irreplaceable role in maintaining animal body movement and regulating metabolism. Therefore, it is of great significance to explore its growth, development and regeneration to improve the meat yield and quality of livestock. In this study, we attempted to use RNA-seq and ATAC-seq techniques to identify differentially expressed genes (DEGs) specifically expressed in bovine skeletal muscle as potential candidates for studying the regulatory mechanisms of muscle development. Microarray data from 8 tissue samples were selected from the GEO database for analysis. First, we obtained gene modules related to each tissue through WGCNA analysis. Through Gene Ontology (GO) functional annotation, the module of lightyellow (MElightyellow) was closely related to muscle development, and 213 hub genes were screened as follow-up research targets. Further, the difference analysis showed that, except for PREB, all other candidate hub genes were up-regulated (muscle group vs. other-group). ATAC-seq analysis showed that muscle-specific accessible chromatin regions were mainly located in promoter of genes related to muscle structure development (GO:0061061), muscle cell development (GO:0055001) and muscle system process (GO:0003012), which were involved in cAMP, CGMP-PKG, MAPK, and other signaling pathways. Next, we integrated the results of RNA-seq and ATAC-seq analysis, and 54 of the 212 candidate hub genes were identified as key regulatory genes in skeletal muscle development. Finally, through motif analysis, 22 of the 54 key genes were found to be potential target genes of transcription factor MEF2C. Including CAPN3, ACTN2, MB, MYOM3, SRL, CKM, ALPK3, MAP3K20, UBE2G1, NEURL2, CAND2, DOT1L, HRC, MAMSTR, FSD2, LRRC2, LSMEM1, SLC29A2, FHL3, KLHL41, ATXN7L2, and PDRG1. This provides a potential reference for studying the molecular mechanism of skeletal muscle development in mammals.
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Affiliation(s)
- Jianfang Wang
- College of Animal Science and Technology, Northwest A&F University, Xianyang, China
| | - Bingzhi Li
- College of Animal Science and Technology, Northwest A&F University, Xianyang, China
| | - Xinran Yang
- College of Animal Science and Technology, Northwest A&F University, Xianyang, China
| | - Chengcheng Liang
- College of Animal Science and Technology, Northwest A&F University, Xianyang, China
| | | | - Yueting Pan
- College of Animal Science and Technology, Northwest A&F University, Xianyang, China
| | - Ke Zhang
- College of Animal Science and Technology, Northwest A&F University, Xianyang, China
| | - Linsen Zan
- College of Animal Science and Technology, Northwest A&F University, Xianyang, China
- National Beef Cattle Improvement Center, Northwest A&F University, Xianyang, China
- *Correspondence: Linsen Zan
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27
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Li J, Gou Y, Yang J, Zhao L, Wang B, Hao T, Sun J. Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis. BMC Genomics 2022; 23:475. [PMID: 35764922 PMCID: PMC9238104 DOI: 10.1186/s12864-022-08698-z] [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] [Received: 09/17/2021] [Accepted: 06/06/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed.
Results
In this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis.
Conclusion
The largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value.
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28
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Investigating chromatin accessibility during development and differentiation by ATAC-sequencing to guide the identification of cis-regulatory elements. Biochem Soc Trans 2022; 50:1167-1177. [PMID: 35604124 PMCID: PMC9246326 DOI: 10.1042/bst20210834] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 11/17/2022]
Abstract
Mapping accessible chromatin across time scales can give insights into its dynamic nature, for example during cellular differentiation and tissue or organism development. Analysis of such data can be utilised to identify functional cis-regulatory elements (CRE) and transcription factor binding sites and, when combined with transcriptomics, can reveal gene regulatory networks (GRNs) of expressed genes. Chromatin accessibility mapping is a powerful approach and can be performed using ATAC-sequencing (ATAC-seq), whereby Tn5 transposase inserts sequencing adaptors into genomic DNA to identify differentially accessible regions of chromatin in different cell populations. It requires low sample input and can be performed and analysed relatively quickly compared with other methods. The data generated from ATAC-seq, along with other genomic approaches, can help uncover chromatin packaging and potential cis-regulatory elements that may be responsible for gene expression. Here, we describe the ATAC-seq approach and give examples from mainly vertebrate embryonic development, where such datasets have identified the highly dynamic nature of chromatin, with differing landscapes between cellular precursors for different lineages.
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29
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Sin ST, Deng J, Ji L, Yukawa M, Chan RW, Volpi S, Vaglio A, Fenaroli P, Bocca P, Cheng SH, Wong DK, Lui KO, Jiang P, Chan KCA, Chiu RW, Lo YMD. Effects of nucleases on cell-free extrachromosomal circular DNA. JCI Insight 2022; 7:156070. [PMID: 35451374 PMCID: PMC9089787 DOI: 10.1172/jci.insight.156070] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/09/2022] [Indexed: 01/09/2023] Open
Abstract
Cell-free extrachromosomal circular DNA (eccDNA) as a distinct topological form from linear DNA has recently gained increasing research interest, with possible clinical applications as a class of biomarkers. In this study, we aimed to explore the relationship between nucleases and eccDNA characteristics in plasma. By using knockout mouse models with deficiencies in deoxyribonuclease 1 (DNASE1) or deoxyribonuclease 1 like 3 (DNASE1L3), we found that cell-free eccDNA in Dnase1l3-/- mice exhibited larger size distributions than that in wild-type mice. Such size alterations were not found in tissue eccDNA of either Dnase1-/- or Dnase1l3-/- mice, suggesting that DNASE1L3 could digest eccDNA extracellularly but did not seem to affect intracellular eccDNA. Using a mouse pregnancy model, we observed that in Dnase1l3-/- mice pregnant with Dnase1l3+/- fetuses, the eccDNA in the maternal plasma was shorter compared with that of Dnase1l3-/- mice carrying Dnase1l3-/- fetuses, highlighting the systemic effects of circulating fetal DNASE1L3 degrading the maternal eccDNA extracellularly. Furthermore, plasma eccDNA in patients with DNASE1L3 mutations also exhibited longer size distributions than that in healthy controls. Taken together, this study provided a hitherto missing link between nuclease activity and the biological manifestations of eccDNA in plasma, paving the way for future biomarker development of this special form of DNA molecules.
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Affiliation(s)
- Sarah Tk Sin
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Jiaen Deng
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Lu Ji
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Masashi Yukawa
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Rebecca Wy Chan
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Stefano Volpi
- Pediatric and Rheumatology Clinic, Center of Autoinflammatory Diseases and Immunodeficiencies, Scientific Hospitalization and Treatment Institute (IRCCS), Giannina Gaslini Institute, Genova, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal-Child Sciences (DINOGMI), University of Genova, Genova, Italy
| | - Augusto Vaglio
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio," School of Human Health Sciences, University of Florence, Florence, Italy.,Medical Genetics Unit and.,Nephrology and Dialysis Unit, Meyer Children's Hospital, Florence, Italy
| | | | - Paola Bocca
- Pediatric and Rheumatology Clinic, Center of Autoinflammatory Diseases and Immunodeficiencies, Scientific Hospitalization and Treatment Institute (IRCCS), Giannina Gaslini Institute, Genova, Italy
| | - Suk Hang Cheng
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Danny Kl Wong
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Kathy O Lui
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Peiyong Jiang
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - K C Allen Chan
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Rossa Wk Chiu
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Y M Dennis Lo
- Li Ka Shing Institute of Health Sciences and.,Department of Chemical Pathology, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Centre for Novostics, Hong Kong Science Park, the Chinese University of Hong Kong, Pak Shek Kok, New Territories, Hong Kong SAR, China
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30
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Kaplow IM, Schäffer DE, Wirthlin ME, Lawler AJ, Brown AR, Kleyman M, Pfenning AR. Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin. BMC Genomics 2022; 23:291. [PMID: 35410163 PMCID: PMC8996547 DOI: 10.1186/s12864-022-08450-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 03/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Evolutionary conservation is an invaluable tool for inferring functional significance in the genome, including regions that are crucial across many species and those that have undergone convergent evolution. Computational methods to test for sequence conservation are dominated by algorithms that examine the ability of one or more nucleotides to align across large evolutionary distances. While these nucleotide alignment-based approaches have proven powerful for protein-coding genes and some non-coding elements, they fail to capture conservation of many enhancers, distal regulatory elements that control spatial and temporal patterns of gene expression. The function of enhancers is governed by a complex, often tissue- and cell type-specific code that links combinations of transcription factor binding sites and other regulation-related sequence patterns to regulatory activity. Thus, function of orthologous enhancer regions can be conserved across large evolutionary distances, even when nucleotide turnover is high. RESULTS We present a new machine learning-based approach for evaluating enhancer conservation that leverages the combinatorial sequence code of enhancer activity rather than relying on the alignment of individual nucleotides. We first train a convolutional neural network model that can predict tissue-specific open chromatin, a proxy for enhancer activity, across mammals. Next, we apply that model to distinguish instances where the genome sequence would predict conserved function versus a loss of regulatory activity in that tissue. We present criteria for systematically evaluating model performance for this task and use them to demonstrate that our models accurately predict tissue-specific conservation and divergence in open chromatin between primate and rodent species, vastly out-performing leading nucleotide alignment-based approaches. We then apply our models to predict open chromatin at orthologs of brain and liver open chromatin regions across hundreds of mammals and find that brain enhancers associated with neuron activity have a stronger tendency than the general population to have predicted lineage-specific open chromatin. CONCLUSION The framework presented here provides a mechanism to annotate tissue-specific regulatory function across hundreds of genomes and to study enhancer evolution using predicted regulatory differences rather than nucleotide-level conservation measurements.
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Affiliation(s)
- Irene M Kaplow
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Daniel E Schäffer
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Morgan E Wirthlin
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alyssa J Lawler
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ashley R Brown
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Michael Kleyman
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Andreas R Pfenning
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
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31
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Optimization of the Omni-ATAC protocol to chromatin accessibility profiling in snap-frozen rat adipose and muscle tissues. MethodsX 2022; 9:101681. [PMID: 35464805 PMCID: PMC9027329 DOI: 10.1016/j.mex.2022.101681] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/22/2022] [Indexed: 01/11/2023] Open
Abstract
ATAC-seq is a fast and sensitive method for the epigenomic profiling of open chromatin and for mapping of transcription factor binding sites [1]. Despite the development of the Omni-ATAC protocol for the profiling of chromatin accessibility in frozen tissues [2], studies in adipose tissue have been restricted due to technical challenges including the high lipid content of adipocytes and reproducibility issues between replicates. Here, we provide a modified Omni-ATAC protocol that achieves high data reproducibility in various tissue types from rat, including adipose and muscle tissues [3].•This protocol describes a methodology that enables chromatin accessibility profiling from snap-frozen rat adipose and muscle tissues.•The technique comprises an optimized bead-based tissue homogenization process that substitutes to Dounce homogenization, reduces variability in the experimental procedure, and is adaptable to various tissue types.•In comparison with the Omni-ATAC protocol, the method described here results in improved ATAC-seq data quality that complies with ENCODE quality standards.
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32
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Habash NW, Sehrawat TS, Shah VH, Cao S. Epigenetics of alcohol-related liver diseases. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100466. [PMID: 35462859 PMCID: PMC9018389 DOI: 10.1016/j.jhepr.2022.100466] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/14/2022] [Accepted: 02/22/2022] [Indexed: 02/07/2023]
Abstract
Alcohol-related liver disease (ARLD) is a primary cause of chronic liver disease in the United States. Despite advances in the diagnosis and management of ARLD, it remains a major public health problem associated with significant morbidity and mortality, emphasising the need to adopt novel approaches to the study of ARLD and its complications. Epigenetic changes are increasingly being recognised as contributing to the pathogenesis of multiple disease states. Harnessing the power of innovative technologies for the study of epigenetics (e.g., next-generation sequencing, DNA methylation assays, histone modification profiling and computational techniques like machine learning) has resulted in a seismic shift in our understanding of the pathophysiology of ARLD. Knowledge of these techniques and advances is of paramount importance for the practicing hepatologist and researchers alike. Accordingly, in this review article we will summarise the current knowledge about alcohol-induced epigenetic alterations in the context of ARLD, including but not limited to, DNA hyper/hypo methylation, histone modifications, changes in non-coding RNA, 3D chromatin architecture and enhancer-promoter interactions. Additionally, we will discuss the state-of-the-art techniques used in the study of ARLD (e.g. single-cell sequencing). We will also highlight the epigenetic regulation of chemokines and their proinflammatory role in the context of ARLD. Lastly, we will examine the clinical applications of epigenetics in the diagnosis and management of ARLD.
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Key Words
- 3C, chromosome conformation capture
- 4C, chromosome conformation capture-on-chip
- AH, alcohol-related hepatitis
- ARLD, alcohol-related liver disease
- ASH, alcohol-related steatohepatitis
- ATAC, assay for transposase-accessible chromatin
- Acetylation
- Alcohol liver disease
- BET, bromodomain and extraterminal motif
- BETi, BET inhibitor
- BRD, bromodomain
- CCL2, C-C motif chemokine ligand 2
- CTCF, CCCTC-binding factor
- CXCL, C-X-C motif chemokine ligand
- Chromatin architecture
- Computational biology
- DNA methylation
- DNMT, DNA methyltransferase
- E-P, enhancer-promoter
- Epidrugs
- Epigenetics
- FKBP5, FK506-binding protein 5
- HCC, hepatocellular carcinoma
- HDAC, histone deacetylase
- HIF1α, hypoxia inducible factor-1α
- HMGB1, high-mobility group box protein 1
- HNF4α, hepatocyte nuclear factor 4α
- HSC, hepatic stellate cell
- Hi-C, chromosome capture followed by high-throughput sequencing
- Histones
- IL, interleukin
- LPS, lipopolysaccharide
- MALAT1, metastasis-associated lung adenocarcinoma transcript 1
- MECP2, methyl-CpG binding protein 2
- NAFLD, non-alcohol-related fatty liver disease
- PPARG, peroxisome proliferator activated receptor-γ
- SAA, salvianolic acid A
- SIRT, sirtuin
- SREBPs, sterol regulatory element-binding proteins
- Single cell epigenome
- TAD, topologically associating domain
- TEAD, TEA domain transcription factor
- TLR, Toll-like receptor
- TNF, tumour necrosis factor
- YAP, Yes-associated protein
- lncRNA, long non-coding RNA
- miRNA, microRNA
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Affiliation(s)
| | | | - Vijay H. Shah
- Corresponding authors. Address: Division of Gastroenterology and Hepatology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. Tel. 507-255-6028, fax: 507-255-6318.
| | - Sheng Cao
- Corresponding authors. Address: Division of Gastroenterology and Hepatology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. Tel. 507-255-6028, fax: 507-255-6318.
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Sahinyan K, Blackburn DM, Simon MM, Lazure F, Kwan T, Bourque G, Soleimani VD. Application of ATAC-Seq for genome-wide analysis of the chromatin state at single myofiber resolution. eLife 2022; 11:72792. [PMID: 35188098 PMCID: PMC8901173 DOI: 10.7554/elife.72792] [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: 08/04/2021] [Accepted: 02/09/2022] [Indexed: 12/11/2022] Open
Abstract
Myofibers are the main components of skeletal muscle, which is the largest tissue in the body. Myofibers are highly adaptive and can be altered under different biological and disease conditions. Therefore, transcriptional and epigenetic studies on myofibers are crucial to discover how chromatin alterations occur in the skeletal muscle under different conditions. However, due to the heterogenous nature of skeletal muscle, studying myofibers in isolation proves to be a challenging task. Single-cell sequencing has permitted the study of the epigenome of isolated myonuclei. While this provides sequencing with high dimensionality, the sequencing depth is lacking, which makes comparisons between different biological conditions difficult. Here, we report the first implementation of single myofiber ATAC-Seq, which allows for the sequencing of an individual myofiber at a depth sufficient for peak calling and for comparative analysis of chromatin accessibility under various physiological and disease conditions. Application of this technique revealed significant differences in chromatin accessibility between resting and regenerating myofibers, as well as between myofibers from a mouse model of Duchenne Muscular Dystrophy (mdx) and wild-type (WT) counterparts. This technique can lead to a wide application in the identification of chromatin regulatory elements and epigenetic mechanisms in muscle fibers during development and in muscle-wasting diseases.
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Affiliation(s)
- Korin Sahinyan
- Department of Human Genetics, McGill University, Montreal, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
| | - Darren M Blackburn
- Department of Human Genetics, McGill University, Montreal, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
| | - Marie-Michelle Simon
- Department of Human Genetics, McGill University, Montreal, Canada.,McGill Genome Centre, Montreal, Canada
| | - Felicia Lazure
- Department of Human Genetics, McGill University, Montreal, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
| | - Tony Kwan
- Department of Human Genetics, McGill University, Montreal, Canada.,McGill Genome Centre, Montreal, Canada
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montreal, Canada.,McGill Genome Centre, Montreal, Canada.,Canadian Centre for Computational Genomics, Montreal, Canada
| | - Vahab D Soleimani
- Department of Human Genetics, McGill University, Montreal, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
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Abstract
The Human Genome Project marked a major milestone in the scientific community as it unravelled the ~3 billion bases that are central to crucial aspects of human life. Despite this achievement, it only scratched the surface of understanding how each nucleotide matters, both individually and as part of a larger unit. Beyond the coding genome, which comprises only ~2% of the whole genome, scientists have realized that large portions of the genome, not known to code for any protein, were crucial for regulating the coding genes. These large portions of the genome comprise the 'non-coding genome'. The history of gene regulation mediated by proteins that bind to the regulatory non-coding genome dates back many decades to the 1960s. However, the original definition of 'enhancers' was first used in the early 1980s. In this Review, we summarize benchmark studies that have mapped the role of cardiac enhancers in disease and development. We highlight instances in which enhancer-localized genetic variants explain the missing link to cardiac pathogenesis. Finally, we inspire readers to consider the next phase of exploring enhancer-based gene therapy for cardiovascular disease.
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35
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Rocks D, Jaric I, Tesfa L, Greally JM, Suzuki M, Kundakovic M. Cell type-specific chromatin accessibility analysis in the mouse and human brain. Epigenetics 2022; 17:202-219. [PMID: 33775205 PMCID: PMC8865312 DOI: 10.1080/15592294.2021.1896983] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/18/2021] [Accepted: 01/30/2021] [Indexed: 11/07/2022] Open
Abstract
The Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) is becoming popular in the neuroscience field where chromatin regulation is thought to be involved in neurodevelopment, activity-dependent gene regulation, hormonal and environmental responses, and pathophysiology of neuropsychiatric disorders. The advantages of using ATAC-seq include a small amount of material needed, fast protocol, and the ability to capture a range of gene regulatory elements with a single assay. With increasing interest in chromatin research, it is an imperative to have feasible, reliable assays that are compatible with a range of neuroscience study designs. Here we tested three protocols for neuronal chromatin accessibility analysis, including a varying brain tissue freezing method followed by fluorescence-activated nuclei sorting (FANS) and ATAC-seq. Our study shows that the cryopreservation method impacts the number of open chromatin regions identified from frozen brain tissue using ATAC-seq. However, we show that all protocols generate consistent and robust data and enable the identification of functional regulatory elements in neuronal cells. Our study implies that the broad biological interpretation of chromatin accessibility data is not significantly affected by the freezing condition. We also reveal additional challenges of doing chromatin analysis on post-mortem human brain tissue. Overall, ATAC-seq coupled with FANS is a powerful method to capture cell-type-specific chromatin accessibility information in mouse and human brain. Our study provides alternative brain preservation methods that generate high-quality ATAC-seq data while fitting in different study designs, and further encourages the use of this method to uncover the role of epigenetic (dys)regulation in the brain.
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Affiliation(s)
- Devin Rocks
- Department of Biological Sciences, Fordham University, Bronx, NY, USA
| | - Ivana Jaric
- Department of Biological Sciences, Fordham University, Bronx, NY, USA
| | - Lydia Tesfa
- Flow Cytometry Core Facility, Albert Einstein College of Medicine, Bronx, NY, USA
| | - John M. Greally
- Center for Epigenomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Masako Suzuki
- Center for Epigenomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Marija Kundakovic
- Department of Biological Sciences, Fordham University, Bronx, NY, USA
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36
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Moyle LA, Davoudi S, Gilbert PM. Innovation in culture systems to study muscle complexity. Exp Cell Res 2021; 411:112966. [PMID: 34906582 DOI: 10.1016/j.yexcr.2021.112966] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 10/31/2021] [Accepted: 12/04/2021] [Indexed: 11/19/2022]
Abstract
Endogenous skeletal muscle development, regeneration, and pathology are extremely complex processes, influenced by local and systemic factors. Unpinning how these mechanisms function is crucial for fundamental biology and to develop therapeutic interventions for genetic disorders, but also conditions like sarcopenia and volumetric muscle loss. Ex vivo skeletal muscle models range from two- and three-dimensional primary cultures of satellite stem cell-derived myoblasts grown alone or in co-culture, to single muscle myofibers, myobundles, and whole tissues. Together, these systems provide the opportunity to gain mechanistic insights of stem cell behavior, cell-cell interactions, and mature muscle function in simplified systems, without confounding variables. Here, we highlight recent advances (published in the last 5 years) using in vitro primary cells and ex vivo skeletal muscle models, and summarize the new insights, tools, datasets, and screening methods they have provided. Finally, we highlight the opportunity for exponential advance of skeletal muscle knowledge, with spatiotemporal resolution, that is offered by guiding the study of muscle biology and physiology with in silico modelling and implementing high-content cell biology systems and ex vivo physiology platforms.
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Affiliation(s)
- Louise A Moyle
- Institute of Biomedical Engineering, Toronto, ON, M5S 3G9, Canada; Donnelly Centre for Cellular and Biomolecular Research, Toronto, ON, M5S 3E1, Canada
| | - Sadegh Davoudi
- Institute of Biomedical Engineering, Toronto, ON, M5S 3G9, Canada; Donnelly Centre for Cellular and Biomolecular Research, Toronto, ON, M5S 3E1, Canada
| | - Penney M Gilbert
- Institute of Biomedical Engineering, Toronto, ON, M5S 3G9, Canada; Donnelly Centre for Cellular and Biomolecular Research, Toronto, ON, M5S 3E1, Canada; Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 1A8, Canada.
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37
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Gao W, Gallardo-Dodd CJ, Kutter C. Cell type-specific analysis by single-cell profiling identifies a stable mammalian tRNA-mRNA interface and increased translation efficiency in neurons. Genome Res 2021; 32:97-110. [PMID: 34857654 PMCID: PMC8744671 DOI: 10.1101/gr.275944.121] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 11/24/2021] [Indexed: 11/24/2022]
Abstract
The correlation between codon and anticodon pools influences the efficiency of translation, but whether differences exist in these pools across individual cells is unknown. We determined that codon usage and amino acid demand are highly stable across different cell types using available mouse and human single-cell RNA-sequencing atlases. After showing the robustness of ATAC-sequencing measurements for the analysis of tRNA gene usage, we quantified anticodon usage and amino acid supply in both mouse and human single-cell ATAC-seq atlases. We found that tRNA gene usage is overall coordinated across cell types, except in neurons, which clustered separately from other cell types. Integration of these data sets revealed a strong and statistically significant correlation between amino acid supply and demand across almost all cell types. Neurons have an enhanced translation efficiency over other cell types, driven by an increased supply of tRNAAla (AGC) anticodons. This results in faster decoding of the Ala-GCC codon, as determined by cell type–specific ribosome profiling, suggesting that the reduction of tRNAAla (AGC) anticodon pools may be implicated in neurological pathologies. This study, the first such examination of codon usage, anticodon usage, and translation efficiency resolved at the cell-type level with single-cell information, identifies a conserved landscape of translation elongation across mammalian cellular diversity and evolution.
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Affiliation(s)
- William Gao
- Department of Microbiology, Tumor, and Cell Biology, Karolinska Institute, Science for Life Laboratory, 171 77, Stockholm, Sweden
| | - Carlos J Gallardo-Dodd
- Department of Microbiology, Tumor, and Cell Biology, Karolinska Institute, Science for Life Laboratory, 171 77, Stockholm, Sweden
| | - Claudia Kutter
- Department of Microbiology, Tumor, and Cell Biology, Karolinska Institute, Science for Life Laboratory, 171 77, Stockholm, Sweden
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38
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Bengtsen M, Winje IM, Eftestøl E, Landskron J, Sun C, Nygård K, Domanska D, Millay DP, Meza-Zepeda LA, Gundersen K. Comparing the epigenetic landscape in myonuclei purified with a PCM1 antibody from a fast/glycolytic and a slow/oxidative muscle. PLoS Genet 2021; 17:e1009907. [PMID: 34752468 PMCID: PMC8604348 DOI: 10.1371/journal.pgen.1009907] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 11/19/2021] [Accepted: 10/23/2021] [Indexed: 01/04/2023] Open
Abstract
Muscle cells have different phenotypes adapted to different usage, and can be grossly divided into fast/glycolytic and slow/oxidative types. While most muscles contain a mixture of such fiber types, we aimed at providing a genome-wide analysis of the epigenetic landscape by ChIP-Seq in two muscle extremes, the fast/glycolytic extensor digitorum longus (EDL) and slow/oxidative soleus muscles. Muscle is a heterogeneous tissue where up to 60% of the nuclei can be of a different origin. Since cellular homogeneity is critical in epigenome-wide association studies we developed a new method for purifying skeletal muscle nuclei from whole tissue, based on the nuclear envelope protein Pericentriolar material 1 (PCM1) being a specific marker for myonuclei. Using antibody labelling and a magnetic-assisted sorting approach, we were able to sort out myonuclei with 95% purity in muscles from mice, rats and humans. The sorting eliminated influence from the other cell types in the tissue and improved the myo-specific signal. A genome-wide comparison of the epigenetic landscape in EDL and soleus reflected the differences in the functional properties of the two muscles, and revealed distinct regulatory programs involving distal enhancers, including a glycolytic super-enhancer in the EDL. The two muscles were also regulated by different sets of transcription factors; e.g. in soleus, binding sites for MEF2C, NFATC2 and PPARA were enriched, while in EDL MYOD1 and SIX1 binding sites were found to be overrepresented. In addition, more novel transcription factors for muscle regulation such as members of the MAF family, ZFX and ZBTB14 were identified. Complex tissues like skeletal muscle contain a variety of cells which confound the analysis of each cell type when based on homogenates, thus only about half of the cell nuclei in muscles reside inside the muscle cells. We here describe a labelling and sorting technique that allowed us to study the epigenetic landscape in purified muscle cell nuclei leaving the other cell types out. Differences between a fast/glycolytic and a slow/oxidative muscle were studied. While all skeletal muscle fibers have a similar make up and basic function, they differ in their physiology and the way they are used. Thus, some fibers are fast contracting but fatigable, and are used for short lasting explosive tasks such as sprinting. Other fibers are slow and are used for more prolonged tasks such as standing or long distance running. Since fiber type correlate with metabolic profile these features can also be related to metabolic diseases. We here show that the epigenetic landscape differed in gene loci corresponding to the differences in functional properties, and revealed that the two types are enriched in different gene regulatory networks. Exercise can alter muscle phenotype, and the epigenetic landscape might be related to how plastic different properties are.
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Affiliation(s)
- Mads Bengtsen
- Department of Biosciences, University of Oslo, Oslo, Norway
| | | | - Einar Eftestøl
- Department of Biosciences, University of Oslo, Oslo, Norway
- Division of Molecular Cardiovascular Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | | | - Chengyi Sun
- Division of Molecular Cardiovascular Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Kamilla Nygård
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Diana Domanska
- Department of Pathology, University of Oslo, Oslo, Norway
| | - Douglas P. Millay
- Division of Molecular Cardiovascular Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Leonardo A. Meza-Zepeda
- Department of Core Facilities, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
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39
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Qu M, Qu H, Jia Z, Kay SA. HNF4A defines tissue-specific circadian rhythms by beaconing BMAL1::CLOCK chromatin binding and shaping the rhythmic chromatin landscape. Nat Commun 2021; 12:6350. [PMID: 34732735 PMCID: PMC8566521 DOI: 10.1038/s41467-021-26567-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
Transcription modulated by the circadian clock is diverse across cell types, underlying circadian control of peripheral metabolism and its observed perturbation in human diseases. We report that knockout of the lineage-specifying Hnf4a gene in mouse liver causes associated reductions in the genome-wide distribution of core clock component BMAL1 and accessible chromatin marks (H3K4me1 and H3K27ac). Ectopically expressing HNF4A remodels chromatin landscape and nucleates distinct tissue-specific BMAL1 chromatin binding events, predominantly in enhancer regions. Circadian rhythms are disturbed in Hnf4a knockout liver and HNF4A-MODY diabetic model cells. Additionally, the epigenetic state and accessibility of the liver genome dynamically change throughout the day, synchronized with chromatin occupancy of HNF4A and clustered expression of circadian outputs. Lastly, Bmal1 knockout attenuates HNF4A genome-wide binding in the liver, likely due to downregulated Hnf4a transcription. Our results may provide a general mechanism for establishing circadian rhythm heterogeneity during development and disease progression, governed by chromatin structure.
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Affiliation(s)
- Meng Qu
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
| | - Han Qu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Zhenyu Jia
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
- Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, CA, 92521, USA
| | - Steve A Kay
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
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40
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Maehara K, Tomimatsu K, Harada A, Tanaka K, Sato S, Fukuoka M, Okada S, Handa T, Kurumizaka H, Saitoh N, Kimura H, Ohkawa Y. Modeling population size independent tissue epigenomes by ChIL-seq with single thin sections. Mol Syst Biol 2021; 17:e10323. [PMID: 34730297 PMCID: PMC8564819 DOI: 10.15252/msb.202110323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 09/29/2021] [Accepted: 10/01/2021] [Indexed: 11/25/2022] Open
Abstract
Recent advances in genome-wide technologies have enabled analyses using small cell numbers of even single cells. However, obtaining tissue epigenomes with cell-type resolution from large organs and tissues still remains challenging, especially when the available material is limited. Here, we present a ChIL-based approach for analyzing the diverse cellular dynamics at the tissue level using high-depth epigenomic data. "ChIL for tissues" allows the analysis of a single tissue section and can reproducibly generate epigenomic profiles from several tissue types, based on the distribution of target epigenomic states, tissue morphology, and number of cells. The proposed method enabled the independent evaluation of changes in cell populations and gene activation in cells from regenerating skeletal muscle tissues, using a statistical model of RNA polymerase II distribution on gene loci. Thus, the integrative analyses performed using ChIL can elucidate in vivo cell-type dynamics of tissues.
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Affiliation(s)
- Kazumitsu Maehara
- Division of TranscriptomicsMedical Institute of BioregulationKyushu UniversityFukuokaJapan
| | - Kosuke Tomimatsu
- Division of TranscriptomicsMedical Institute of BioregulationKyushu UniversityFukuokaJapan
| | - Akihito Harada
- Division of TranscriptomicsMedical Institute of BioregulationKyushu UniversityFukuokaJapan
| | - Kaori Tanaka
- Division of TranscriptomicsMedical Institute of BioregulationKyushu UniversityFukuokaJapan
| | - Shoko Sato
- Laboratory of Chromatin Structure and FunctionInstitute for Quantitative BiosciencesThe University of TokyoTokyoJapan
| | - Megumi Fukuoka
- Division of Cancer BiologyThe Cancer Institute of Japanese Foundation for Cancer ResearchTokyoJapan
| | - Seiji Okada
- Division of PathophysiologyMedical Institute of BioregulationKyushu UniversityFukuokaJapan
| | - Tetsuya Handa
- Cell Biology CenterInstitute of Innovative ResearchTokyo Institute of TechnologyYokohamaJapan
| | - Hitoshi Kurumizaka
- Laboratory of Chromatin Structure and FunctionInstitute for Quantitative BiosciencesThe University of TokyoTokyoJapan
| | - Noriko Saitoh
- Division of Cancer BiologyThe Cancer Institute of Japanese Foundation for Cancer ResearchTokyoJapan
| | - Hiroshi Kimura
- Cell Biology CenterInstitute of Innovative ResearchTokyo Institute of TechnologyYokohamaJapan
| | - Yasuyuki Ohkawa
- Division of TranscriptomicsMedical Institute of BioregulationKyushu UniversityFukuokaJapan
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41
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Talon I, Janiszewski A, Theeuwes B, Lefevre T, Song J, Bervoets G, Vanheer L, De Geest N, Poovathingal S, Allsop R, Marine JC, Rambow F, Voet T, Pasque V. Enhanced chromatin accessibility contributes to X chromosome dosage compensation in mammals. Genome Biol 2021; 22:302. [PMID: 34724962 PMCID: PMC8558763 DOI: 10.1186/s13059-021-02518-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/13/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Precise gene dosage of the X chromosomes is critical for normal development and cellular function. In mice, XX female somatic cells show transcriptional X chromosome upregulation of their single active X chromosome, while the other X chromosome is inactive. Moreover, the inactive X chromosome is reactivated during development in the inner cell mass and in germ cells through X chromosome reactivation, which can be studied in vitro by reprogramming of somatic cells to pluripotency. How chromatin processes and gene regulatory networks evolved to regulate X chromosome dosage in the somatic state and during X chromosome reactivation remains unclear. RESULTS Using genome-wide approaches, allele-specific ATAC-seq and single-cell RNA-seq, in female embryonic fibroblasts and during reprogramming to pluripotency, we show that chromatin accessibility on the upregulated mammalian active X chromosome is increased compared to autosomes. We further show that increased accessibility on the active X chromosome is erased by reprogramming, accompanied by erasure of transcriptional X chromosome upregulation and the loss of increased transcriptional burst frequency. In addition, we characterize gene regulatory networks during reprogramming and X chromosome reactivation, revealing changes in regulatory states. Our data show that ZFP42/REX1, a pluripotency-associated gene that evolved specifically in placental mammals, targets multiple X-linked genes, suggesting an evolutionary link between ZFP42/REX1, X chromosome reactivation, and pluripotency. CONCLUSIONS Our data reveal the existence of intrinsic compensatory mechanisms that involve modulation of chromatin accessibility to counteract X-to-Autosome gene dosage imbalances caused by evolutionary or in vitro X chromosome loss and X chromosome inactivation in mammalian cells.
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Affiliation(s)
- Irene Talon
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
| | - Adrian Janiszewski
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
| | - Bart Theeuwes
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
| | - Thomas Lefevre
- Laboratory of Reproductive Genomics, Centre for Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Juan Song
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
| | - Greet Bervoets
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, VIB, 3000 Leuven, Belgium
- Department of Oncology, Laboratory for Molecular Cancer Biology, KU Leuven, 3000 Leuven, Belgium
| | - Lotte Vanheer
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
| | - Natalie De Geest
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
| | - Suresh Poovathingal
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
| | - Ryan Allsop
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
| | - Jean-Christophe Marine
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, VIB, 3000 Leuven, Belgium
- Department of Oncology, Laboratory for Molecular Cancer Biology, KU Leuven, 3000 Leuven, Belgium
| | - Florian Rambow
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, VIB, 3000 Leuven, Belgium
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Laboratory of Reproductive Genomics, Centre for Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Vincent Pasque
- Department of Development and Regeneration, Laboratory of Cellular Reprogramming and Epigenetic Regulation, KU Leuven – University of Leuven, Herestraat 49, 3000 Leuven, Belgium
- KU Leuven Institute for Single Cell Omics (LISCO), 3000 Leuven, Belgium
- Leuven Stem Cell Institute (SCIL), 3000 Leuven, Belgium
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42
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Nair VD, Vasoya M, Nair V, Smith GR, Pincas H, Ge Y, Douglas CM, Esser KA, Sealfon SC. Differential analysis of chromatin accessibility and gene expression profiles identifies cis-regulatory elements in rat adipose and muscle. Genomics 2021; 113:3827-3841. [PMID: 34547403 DOI: 10.1016/j.ygeno.2021.09.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 09/08/2021] [Accepted: 09/15/2021] [Indexed: 01/04/2023]
Abstract
Chromatin accessibility is a key factor influencing gene expression. We optimized the Omni-ATAC-seq protocol and used it together with RNA-seq to investigate cis-regulatory elements in rat white adipose and skeletal muscle, two tissues with contrasting metabolic functions. While promoter accessibility correlated with RNA expression, integration of the two datasets identified tissue-specific differentially accessible regions (DARs) that predominantly localized in intergenic and intron regions. DARs were mapped to differentially expressed (DE) genes enriched in distinct biological processes in each tissue. Randomly selected DE genes were validated by qPCR. Top enriched motifs in DARs predicted binding sites for transcription factors (TFs) showing tissue-specific up-regulation. The correlation between differential chromatin accessibility at a given TF binding motif and differential expression of target genes further supported the functional relevance of that motif. Our study identified cis-regulatory regions that likely play a major role in the regulation of tissue-specific gene expression in adipose and muscle.
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Affiliation(s)
- Venugopalan D Nair
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Mital Vasoya
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Vishnu Nair
- Department of Computer Sciences, Columbia University, New York, NY 10027, USA
| | - Gregory R Smith
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hanna Pincas
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yongchao Ge
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Collin M Douglas
- Department of Physiology and Functional Genomics, University of Florida, Gainesville, FL 32610, USA
| | - Karyn A Esser
- Department of Physiology and Functional Genomics, University of Florida, Gainesville, FL 32610, USA
| | - Stuart C Sealfon
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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43
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Srinivasan C, Phan BN, Lawler AJ, Ramamurthy E, Kleyman M, Brown AR, Kaplow IM, Wirthlin ME, Pfenning AR. Addiction-Associated Genetic Variants Implicate Brain Cell Type- and Region-Specific Cis-Regulatory Elements in Addiction Neurobiology. J Neurosci 2021; 41:9008-9030. [PMID: 34462306 PMCID: PMC8549541 DOI: 10.1523/jneurosci.2534-20.2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 06/18/2021] [Accepted: 07/10/2021] [Indexed: 12/14/2022] Open
Abstract
Recent large genome-wide association studies have identified multiple confident risk loci linked to addiction-associated behavioral traits. Most genetic variants linked to addiction-associated traits lie in noncoding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied stratified linkage disequilibrium score regression to compare genome-wide association studies to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative CREs marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of female and male mouse neuronal subtypes: cortical excitatory, D1, D2, and PV. Last, we developed machine learning models to predict mouse cell type-specific open chromatin, enabling us to further categorize human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuronal subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.SIGNIFICANCE STATEMENT We combine statistical genetic and machine learning techniques to find that the predisposition to for nicotine, alcohol, and cannabis use behaviors can be partially explained by genetic variants in conserved regulatory elements within specific brain regions and neuronal subtypes of the reward system. Our computational framework can flexibly integrate open chromatin data across species to screen for putative causal variants in a cell type- and tissue-specific manner for numerous complex traits.
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Affiliation(s)
- Chaitanya Srinivasan
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - BaDoi N Phan
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Medical Scientist Training Program, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Alyssa J Lawler
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Easwaran Ramamurthy
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Michael Kleyman
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Ashley R Brown
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Irene M Kaplow
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Morgan E Wirthlin
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Andreas R Pfenning
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
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44
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Pan Z, Yao Y, Yin H, Cai Z, Wang Y, Bai L, Kern C, Halstead M, Chanthavixay G, Trakooljul N, Wimmers K, Sahana G, Su G, Lund MS, Fredholm M, Karlskov-Mortensen P, Ernst CW, Ross P, Tuggle CK, Fang L, Zhou H. Pig genome functional annotation enhances the biological interpretation of complex traits and human disease. Nat Commun 2021; 12:5848. [PMID: 34615879 PMCID: PMC8494738 DOI: 10.1038/s41467-021-26153-7] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 09/20/2021] [Indexed: 02/08/2023] Open
Abstract
The functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide biological insights on tissue-specific regulatory conservation, and by integrating 47 human genome-wide association studies, we demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases.
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Affiliation(s)
- Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Hongwei Yin
- Agricultural Genome Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 518120, Shenzhen, China
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, 8300, Denmark
| | - Ying Wang
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Lijing Bai
- Agricultural Genome Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 518120, Shenzhen, China
| | - Colin Kern
- 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
| | | | - Klaus Wimmers
- Leibniz-Institute for Farm Animal Biology, Dummerstorf, Germany
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, 8300, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, 8300, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, 8300, Denmark
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederikgsberg C, 1870, Denmark
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederikgsberg C, 1870, Denmark
| | - Catherine W Ernst
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Pablo Ross
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | | | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA.
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45
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Biswas A, Narlikar L. A universal framework for detecting cis-regulatory diversity in DNA regulatory regions. Genome Res 2021; 31:1646-1662. [PMID: 34285090 PMCID: PMC8415372 DOI: 10.1101/gr.274563.120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 07/09/2021] [Indexed: 12/02/2022]
Abstract
High-throughput sequencing-based assays measure different biochemical activities pertaining to gene regulation, genome-wide. These activities include transcription factor (TF)–DNA binding, enhancer activity, open chromatin, and more. A major goal is to understand underlying sequence components, or motifs, that can explain the measured activity. It is usually not one motif but a combination of motifs bound by cooperatively acting proteins that confers activity to such regions. Furthermore, regions can be diverse, governed by different combinations of TFs/motifs. Current approaches do not take into account this issue of combinatorial diversity. We present a new statistical framework, cisDIVERSITY, which models regions as diverse modules characterized by combinations of motifs while simultaneously learning the motifs themselves. Because cisDIVERSITY does not rely on knowledge of motifs, modules, cell type, or organism, it is general enough to be applied to regions reported by most high-throughput assays. For example, in enhancer predictions resulting from different assays—GRO-cap, STARR-seq, and those measuring chromatin structure—cisDIVERSITY discovers distinct modules and combinations of TF binding sites, some specific to the assay. From protein–DNA binding data, cisDIVERSITY identifies potential cofactors of the profiled TF, whereas from ATAC-seq data, it identifies tissue-specific regulatory modules. Finally, analysis of single-cell ATAC-seq data suggests that regions open in one cell-state encode information about future states, with certain modules staying open and others closing down in the next time point.
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Affiliation(s)
- Anushua Biswas
- CSIR-National Chemical Laboratory, Academy of Scientific and Innovative Research
| | - Leelavati Narlikar
- CSIR-National Chemical Laboratory, Academy of Scientific and Innovative Research
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46
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Peng S, Bellone R, Petersen JL, Kalbfleisch TS, Finno CJ. Successful ATAC-Seq From Snap-Frozen Equine Tissues. Front Genet 2021; 12:641788. [PMID: 34220931 PMCID: PMC8242358 DOI: 10.3389/fgene.2021.641788] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 05/21/2021] [Indexed: 11/20/2022] Open
Abstract
An assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) has become an increasingly popular method to assess genome-wide chromatin accessibility in isolated nuclei from fresh tissues. However, many biobanks contain only snap-frozen tissue samples. While ATAC-seq has been applied to frozen brain tissues in human, its applicability in a wide variety of tissues in horse remains unclear. The Functional Annotation of Animal Genome (FAANG) project is an international collaboration aimed to provide high quality functional annotation of animal genomes. The equine FAANG initiative has generated a biobank of over 80 tissues from two reference female animals and experiments to begin to characterize tissue specificity of genome function for prioritized tissues have been performed. Due to the logistics of tissue collection and storage, extracting nuclei from a large number of tissues for ATAC-seq at the time of collection is not always practical. To assess the feasibility of using stored frozen tissues for ATAC-seq and to provide a guideline for the equine FAANG project, we compared ATAC-seq results from nuclei isolated from frozen tissue to cryopreserved nuclei (CN) isolated at the time of tissue harvest in liver, a highly cellular homogenous tissue, and lamina, a relatively acellular tissue unique to the horse. We identified 20,000-33,000 accessible chromatin regions in lamina and 22-61,000 in liver, with consistently more peaks identified using CN isolated at time of tissue collection. Our results suggest that frozen tissues are an acceptable substitute when CN are not available. For more challenging tissues such as lamina, nuclei extraction at the time of tissue collection is still preferred for optimal results. Therefore, tissue type and accessibility to intact nuclei should be considered when designing ATAC-seq experiments.
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Affiliation(s)
- Sichong Peng
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Rebecca Bellone
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
- Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Jessica L. Petersen
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Theodore S. Kalbfleisch
- Department of Veterinary Science, Gluck Equine Research Center, University of Kentucky, Lexington, KY, United States
| | - Carrie J. Finno
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
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47
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Yuan Y, Deng Q, Wei X, Liu Y, Lan Q, Jiang Y, Yu Y, Guo P, Xu J, Yu C, Han L, Cheng M, Wu P, Zhang X, Lai Y, Volpe G, Esteban MA, Yang H, Liu C, Liu L. The Chromatin Accessibility Landscape of Adult Rat. Front Genet 2021; 12:651604. [PMID: 34108989 PMCID: PMC8181391 DOI: 10.3389/fgene.2021.651604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/01/2021] [Indexed: 11/19/2022] Open
Affiliation(s)
- Yue Yuan
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | - Qiuting Deng
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | - Xiaoyu Wei
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | - Yang Liu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | | | - Yu Jiang
- First Hospital, Jilin University, Changchun, China
| | - Yeya Yu
- BGI-Shenzhen, Shenzhen, China.,BGI College, Zhengzhou University, Zhengzhou, China
| | - Pengcheng Guo
- College of Veterinary Medicine, Jilin University, Changchun, China
| | - Jiangshan Xu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | - Cong Yu
- BGI-Shenzhen, Shenzhen, China
| | - Lei Han
- BGI-Shenzhen, Shenzhen, China
| | - Mengnan Cheng
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | | | - Xiao Zhang
- College of Veterinary Medicine, Jilin University, Changchun, China
| | - Yiwei Lai
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Giacomo Volpe
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Miguel A Esteban
- BGI-Shenzhen, Shenzhen, China.,First Hospital, Jilin University, Changchun, China.,College of Veterinary Medicine, Jilin University, Changchun, China.,Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China.,Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI-Shenzhen, Shenzhen, China
| | - Chuanyu Liu
- BGI-Shenzhen, Shenzhen, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Longqi Liu
- BGI-Shenzhen, Shenzhen, China.,Shenzhen Bay Laboratory, Shenzhen, China
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48
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Grigoryan A, Pospiech J, Krämer S, Lipka D, Liehr T, Geiger H, Kimura H, Mulaw MA, Florian MC. Attrition of X Chromosome Inactivation in Aged Hematopoietic Stem Cells. Stem Cell Reports 2021; 16:708-716. [PMID: 33798450 PMCID: PMC8072063 DOI: 10.1016/j.stemcr.2021.03.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 01/03/2023] Open
Abstract
During X chromosome inactivation (XCI), the inactive X chromosome (Xi) is recruited to the nuclear lamina at the nuclear periphery. Beside X chromosome reactivation resulting in a highly penetrant aging-like hematopoietic malignancy, little is known about XCI in aged hematopoietic stem cells (HSCs). Here, we demonstrate that LaminA/C defines a distinct repressive nuclear compartment for XCI in young HSCs, and its reduction in aged HSCs correlates with an impairment in the overall control of XCI. Integrated omics analyses reveal higher variation in gene expression, global hypomethylation, and significantly increased chromatin accessibility on the X chromosome (Chr X) in aged HSCs. In summary, our data support the role of LaminA/C in the establishment of a special repressive compartment for XCI in HSCs, which is impaired upon aging.
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Affiliation(s)
- Ani Grigoryan
- Institute of Molecular Medicine and Stem Cell Aging, University of Ulm, Albert-Einstein-Allee 11c, 89081 Ulm, Germany
| | - Johannes Pospiech
- Institute of Molecular Medicine and Stem Cell Aging, University of Ulm, Albert-Einstein-Allee 11c, 89081 Ulm, Germany
| | - Stephen Krämer
- Section Translational Cancer Epigenomics, Division of Translational Medical Oncology, German Cancer Research Center (DKFZ) & National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany; Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany; Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Daniel Lipka
- Section Translational Cancer Epigenomics, Division of Translational Medical Oncology, German Cancer Research Center (DKFZ) & National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
| | - Thomas Liehr
- Jena University Hospital, Friedrich Schiller University, Institute of Human Genetics, Am Klinikum 1, 07747 Jena, Germany
| | - Hartmut Geiger
- Institute of Molecular Medicine and Stem Cell Aging, University of Ulm, Albert-Einstein-Allee 11c, 89081 Ulm, Germany
| | - Hiroshi Kimura
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan
| | - Medhanie A Mulaw
- Institute of Molecular Medicine and Stem Cell Aging, University of Ulm, Albert-Einstein-Allee 11c, 89081 Ulm, Germany; Department of Internal Medicine I, University Hospital Ulm, Ulm, Germany.
| | - Maria Carolina Florian
- Institute of Molecular Medicine and Stem Cell Aging, University of Ulm, Albert-Einstein-Allee 11c, 89081 Ulm, Germany; Stem Cell Aging Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL) and Program for advancing the Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], Av. Gran Via 199-203, 08908, L'Hospitalet de Llobregat, Barcelona, Spain; Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
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Kissane S, Dhandapani V, Orsini L. Protocol for assay of transposase accessible chromatin sequencing in non-model species. STAR Protoc 2021; 2:100341. [PMID: 33659905 PMCID: PMC7896190 DOI: 10.1016/j.xpro.2021.100341] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The assay for transposase accessible chromatin (ATAC-seq) is a method for mapping genome-wide chromatin accessibility. Coupled with high-throughput sequencing, it enables integrative epigenomics analyses. ATAC-seq requires direct access to cell nuclei, a major challenge in non-model species such as small invertebrates, whose soft tissue is surrounded by a protective exoskeleton. Here, we present modifications of the ATAC-seq protocol for applications in small crustaceans, extending applications to non-model species. For complete information on the use and execution of this protocol, please refer to Buenrostro et al. (2013). ATAC-seq modified protocol for applications in non-model species Transposase titration identifies thresholds for optimal transposition at lower costs Minimal number of cells/tissue is identified for low input reactions Proof of concept ATAC-seq analysis for the waterflea Daphnia magna
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Affiliation(s)
- Stephen Kissane
- School of Biosciences, University of Birmingham, Birmingham B15 2TT, UK
| | | | - Luisa Orsini
- School of Biosciences, University of Birmingham, Birmingham B15 2TT, UK
- Corresponding author
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50
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Chen Y, Ding X, Wang S, Ding P, Xu Z, Li J, Wang M, Xiang R, Wang X, Wang H, Feng Q, Qiu J, Wang F, Huang Z, Zhang X, Tang G, Tang S. A single-cell atlas of mouse olfactory bulb chromatin accessibility. J Genet Genomics 2021; 48:147-162. [PMID: 33926839 DOI: 10.1016/j.jgg.2021.02.007] [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: 08/06/2020] [Revised: 01/26/2021] [Accepted: 02/01/2021] [Indexed: 10/21/2022]
Abstract
Olfaction, the sense of smell, is a fundamental trait crucial to many species. The olfactory bulb (OB) plays pivotal roles in processing and transmitting odor information from the environment to the brain. The cellular heterogeneity of the mouse OB has been studied using single-cell RNA sequencing. However, the epigenetic landscape of the mOB remains mostly unexplored. Herein, we apply single-cell assay for transposase-accessible chromatin sequencing to profile the genome-wide chromatin accessibility of 9,549 single cells from the mOB. Based on single-cell epigenetic signatures, mOB cells are classified into 21 clusters corresponding to 11 cell types. We identify distinct sets of putative regulatory elements specific to each cell cluster from which putative target genes and enriched potential functions are inferred. In addition, the transcription factor motifs enriched in each cell cluster are determined to indicate the developmental fate of each cell lineage. Our study provides a valuable epigenetic data set for the mOB at single-cell resolution, and the results can enhance our understanding of regulatory circuits and the therapeutic capacity of the OB at the single-cell level.
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Affiliation(s)
- Yin Chen
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Xiangning Ding
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Shiyou Wang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Peiwen Ding
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Zaoxu Xu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Jiankang Li
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
| | - Mingyue Wang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Rong Xiang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Xiaoling Wang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Haoyu Wang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Qikai Feng
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Jiaying Qiu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China
| | - Feiyue Wang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China; BGI-Shenzhen, Shenzhen 518083, China; School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Zhen Huang
- Southern Center for Biomedical Research and Fujian Key Laboratory of Developmental and Neural Biology, College of Life Sciences, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Xingliang Zhang
- Shenzhen Children's Hospital, Shenzhen 518083, China; Department of Pediatrics, the Affiliated Hospital of Guangdong Medical University, Zhanjiang 524000, China.
| | - Gen Tang
- Shenzhen Children's Hospital, Shenzhen 518083, China.
| | - Shengping Tang
- Shenzhen Children's Hospital, Shenzhen 518083, China; Zunyi Medical University, Zunyi, Guizhou 563099, China; China Medical University, Shenyang, Liaoning 110122, China.
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