1
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Dubois V, Lefebvre P, Staels B, Eeckhoute J. Nuclear receptors: pathophysiological mechanisms and drug targets in liver disease. Gut 2024; 73:1562-1569. [PMID: 38862216 DOI: 10.1136/gutjnl-2023-331741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/18/2024] [Indexed: 06/13/2024]
Abstract
Nuclear receptors (NRs) are ligand-dependent transcription factors required for liver development and function. As a consequence, NRs have emerged as attractive drug targets in a wide range of liver diseases. However, liver dysfunction and failure are linked to loss of hepatocyte identity characterised by deficient NR expression and activities. This might at least partly explain why several pharmacological NR modulators have proven insufficiently efficient to improve liver functionality in advanced stages of diseases such as metabolic dysfunction-associated steatotic liver disease (MASLD). In this perspective, we review the most recent advances in the hepatic NR field and discuss the contribution of multiomic approaches to our understanding of their role in the molecular organisation of an intricated transcriptional regulatory network, as well as in liver intercellular dialogues and interorgan cross-talks. We discuss the potential benefit of novel therapeutic approaches simultaneously targeting multiple NRs, which would not only reactivate the hepatic NR network and restore hepatocyte identity but also impact intercellular and interorgan interplays whose importance to control liver functions is further defined. Finally, we highlight the need of considering individual parameters such as sex and disease stage in the development of NR-based clinical strategies.
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Affiliation(s)
- Vanessa Dubois
- Basic and Translational Endocrinology (BaTE), Department of Basic and Applied Medical Sciences, Ghent University, Gent, Belgium
| | - Philippe Lefebvre
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011-EGID, Lille, France
| | - Bart Staels
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011-EGID, Lille, France
| | - Jerome Eeckhoute
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011-EGID, Lille, France
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2
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Peng D, Cahan P. OneSC: A computational platform for recapitulating cell state transitions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596831. [PMID: 38895453 PMCID: PMC11185539 DOI: 10.1101/2024.05.31.596831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.
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Affiliation(s)
- Da Peng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, Maryland, 21205, USA
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3
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Moll T, Harvey C, Alhathli E, Gornall S, O'Brien D, Cooper-Knock J. Non-coding genome contribution to ALS. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2024; 176:75-86. [PMID: 38802183 DOI: 10.1016/bs.irn.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The majority of amyotrophic lateral sclerosis (ALS) is caused by a complex gene-environment interaction. Despite high estimates of heritability, the genetic basis of disease in the majority of ALS patients are unknown. This limits the development of targeted genetic therapies which require an understanding of patient-specific genetic drivers. There is good evidence that the majority of these missing genetic risk factors are likely to be found within the non-coding genome. However, a major challenge in the discovery of non-coding risk variants is determining which variants are functional in which specific CNS cell type. We summarise current discoveries of ALS-associated genetic drivers within the non-coding genome and we make the case that improved cell-specific annotation of genomic function is required to advance this field, particularly via single-cell epigenetic profiling and spatial transcriptomics. We highlight the example of TBK1 where an apparent paradox exists between pathogenic coding variants which cause loss of protein function, and protective non-coding variants which cause reduced gene expression; the paradox is resolved when it is understood that the non-coding variants are acting primarily via change in gene expression within microglia, and the effect of coding variants is most prominent in neurons. We propose that cell-specific functional annotation of ALS-associated genetic variants will accelerate discovery of the genetic architecture underpinning disease in the vast majority of patients.
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Affiliation(s)
- Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Elham Alhathli
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Sarah Gornall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - David O'Brien
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom.
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4
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Zhang S, Moll T, Rubin-Sigler J, Tu S, Li S, Yuan E, Liu M, Butt A, Harvey C, Gornall S, Alhalthli E, Shaw A, Souza CDS, Ferraiuolo L, Hornstein E, Shelkovnikova T, van Dijk CH, Timpanaro IS, Kenna KP, Zeng J, Tsao PS, Shaw PJ, Ichida JK, Cooper-Knock J, Snyder MP. Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.30.24305115. [PMID: 38633814 PMCID: PMC11023684 DOI: 10.1101/2024.03.30.24305115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- J. Crayton Pruitt Family Department of Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Tobias Moll, and Jasper Rubin-Sigler
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- These authors contributed equally: Sai Zhang, Tobias Moll, and Jasper Rubin-Sigler
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
- These authors contributed equally: Sai Zhang, Tobias Moll, and Jasper Rubin-Sigler
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Shuya Li
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Enming Yuan
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Menghui Liu
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Afreen Butt
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Sarah Gornall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Elham Alhalthli
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Allan Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Cleide Dos Santos Souza
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Laura Ferraiuolo
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Eran Hornstein
- Department of Molecular Genetics and Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Tatyana Shelkovnikova
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Charlotte H. van Dijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ilia S. Timpanaro
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Kevin P. Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Pamela J. Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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5
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Ko BS, Han MH, Kwon MJ, Cha DG, Ji Y, Park ES, Jeon MJ, Kim S, Lee K, Choi YH, Lee J, Torras-Llort M, Yoon KJ, Lee H, Kim JK, Lee SB. Baf-mediated transcriptional regulation of teashirt is essential for the development of neural progenitor cell lineages. Exp Mol Med 2024; 56:422-440. [PMID: 38374207 PMCID: PMC10907700 DOI: 10.1038/s12276-024-01169-3] [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: 03/10/2023] [Revised: 09/20/2023] [Accepted: 12/10/2023] [Indexed: 02/21/2024] Open
Abstract
Accumulating evidence hints heterochromatin anchoring to the inner nuclear membrane as an upstream regulatory process of gene expression. Given that the formation of neural progenitor cell lineages and the subsequent maintenance of postmitotic neuronal cell identity critically rely on transcriptional regulation, it seems possible that the development of neuronal cells is influenced by cell type-specific and/or context-dependent programmed regulation of heterochromatin anchoring. Here, we explored this possibility by genetically disrupting the evolutionarily conserved barrier-to-autointegration factor (Baf) in the Drosophila nervous system. Through single-cell RNA sequencing, we demonstrated that Baf knockdown induces prominent transcriptomic changes, particularly in type I neuroblasts. Among the differentially expressed genes, our genetic analyses identified teashirt (tsh), a transcription factor that interacts with beta-catenin, to be closely associated with Baf knockdown-induced phenotypes that were suppressed by the overexpression of tsh or beta-catenin. We also found that Baf and tsh colocalized in a region adjacent to heterochromatin in type I NBs. Notably, the subnuclear localization pattern remained unchanged when one of these two proteins was knocked down, indicating that both proteins contribute to the anchoring of heterochromatin to the inner nuclear membrane. Overall, this study reveals that the Baf-mediated transcriptional regulation of teashirt is a novel molecular mechanism that regulates the development of neural progenitor cell lineages.
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Affiliation(s)
- Byung Su Ko
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
| | - Myeong Hoon Han
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
| | - Min Jee Kwon
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
| | - Dong Gon Cha
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea
| | - Yuri Ji
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
| | - Eun Seo Park
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea
| | - Min Jae Jeon
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
| | - Somi Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Kyeongho Lee
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
- Convergence Research Advanced Centre for Olfaction, DGIST, Daegu, 42988, Republic of Korea
| | - Yoon Ha Choi
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Jusung Lee
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea
| | | | - Ki-Jun Yoon
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hyosang Lee
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
- Convergence Research Advanced Centre for Olfaction, DGIST, Daegu, 42988, Republic of Korea
| | - Jong Kyoung Kim
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea.
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
| | - Sung Bae Lee
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea.
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6
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Wang P, Wen X, Li H, Lang P, Li S, Lei Y, Shu H, Gao L, Zhao D, Zeng J. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nat Commun 2023; 14:8459. [PMID: 38123534 PMCID: PMC10733330 DOI: 10.1038/s41467-023-44103-3] [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: 02/07/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
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Affiliation(s)
- Peizhuo Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Xiao Wen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Peng Lang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Yipin Lei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, Shaanxi Province, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China.
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7
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Kim D, Tran A, Kim HJ, Lin Y, Yang JYH, Yang P. Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. NPJ Syst Biol Appl 2023; 9:51. [PMID: 37857632 PMCID: PMC10587078 DOI: 10.1038/s41540-023-00312-6] [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: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.
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Affiliation(s)
- Daniel Kim
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Andy Tran
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Yingxin Lin
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Jean Yee Hwa Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
| | - Pengyi Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
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8
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Dubois‐Chevalier J, Gheeraert C, Berthier A, Boulet C, Dubois V, Guille L, Fourcot M, Marot G, Gauthier K, Dubuquoy L, Staels B, Lefebvre P, Eeckhoute J. An extended transcription factor regulatory network controls hepatocyte identity. EMBO Rep 2023; 24:e57020. [PMID: 37424431 PMCID: PMC10481658 DOI: 10.15252/embr.202357020] [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: 02/16/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 07/11/2023] Open
Abstract
Cell identity is specified by a core transcriptional regulatory circuitry (CoRC), typically limited to a small set of interconnected cell-specific transcription factors (TFs). By mining global hepatic TF regulons, we reveal a more complex organization of the transcriptional regulatory network controlling hepatocyte identity. We show that tight functional interconnections controlling hepatocyte identity extend to non-cell-specific TFs beyond the CoRC, which we call hepatocyte identity (Hep-ID)CONNECT TFs. Besides controlling identity effector genes, Hep-IDCONNECT TFs also engage in reciprocal transcriptional regulation with TFs of the CoRC. In homeostatic basal conditions, this translates into Hep-IDCONNECT TFs being involved in fine tuning CoRC TF expression including their rhythmic expression patterns. Moreover, a role for Hep-IDCONNECT TFs in the control of hepatocyte identity is revealed in dedifferentiated hepatocytes where Hep-IDCONNECT TFs are able to reset CoRC TF expression. This is observed upon activation of NR1H3 or THRB in hepatocarcinoma or in hepatocytes subjected to inflammation-induced loss of identity. Our study establishes that hepatocyte identity is controlled by an extended array of TFs beyond the CoRC.
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Affiliation(s)
| | - Céline Gheeraert
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
| | - Alexandre Berthier
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
| | - Clémence Boulet
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
| | - Vanessa Dubois
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
- Basic and Translational Endocrinology (BaTE), Department of Basic and Applied Medical SciencesGhent UniversityGhentBelgium
| | - Loïc Guille
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
| | - Marie Fourcot
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 – UAR 2014 – PLBSLilleFrance
| | - Guillemette Marot
- Univ. Lille, Inria, CHU Lille, ULR 2694 – METRICS: Évaluation des technologies de santé et des pratiques médicalesLilleFrance
| | - Karine Gauthier
- Institut de Génomique Fonctionnelle de Lyon (IGFL), CNRS UMR 5242, INRAE USC 1370, École Normale Supérieure de LyonLyonFrance
| | - Laurent Dubuquoy
- Univ. Lille, Inserm, CHU Lille, U1286 – INFINITE – Institute for Translational Research in InflammationLilleFrance
| | - Bart Staels
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
| | - Philippe Lefebvre
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
| | - Jérôme Eeckhoute
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011‐EGIDLilleFrance
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9
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Amini S, Doyle JJ, Libault M. The evolving definition of plant cell type. FRONTIERS IN PLANT SCIENCE 2023; 14:1271070. [PMID: 37692436 PMCID: PMC10485272 DOI: 10.3389/fpls.2023.1271070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/09/2023] [Indexed: 09/12/2023]
Affiliation(s)
- Sahand Amini
- Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Jeffrey J. Doyle
- School of Integrative Plant Science, Plant Biology Section, Cornell University, Ithaca, NY, United States
- School of Integrative Plant Science, Plant Breeding & Genetics Section, Cornell University, Ithaca, NY, United States
| | - Marc Libault
- Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
- Single Cell Genomics Core Facility, Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, United States
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10
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Arts JA, Laberthonnière C, Lima Cunha D, Zhou H. Single-Cell RNA Sequencing: Opportunities and Challenges for Studies on Corneal Biology in Health and Disease. Cells 2023; 12:1808. [PMID: 37443842 PMCID: PMC10340756 DOI: 10.3390/cells12131808] [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: 06/02/2023] [Revised: 06/27/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
The structure and major cell types of the multi-layer human cornea have been extensively studied. However, various cell states in specific cell types and key genes that define the cell states are not fully understood, hindering our comprehension of corneal homeostasis, related diseases, and therapeutic discovery. Single-cell RNA sequencing is a revolutionary and powerful tool for identifying cell states within tissues such as the cornea. This review provides an overview of current single-cell RNA sequencing studies on the human cornea, highlighting similarities and differences between them, and summarizing the key genes that define corneal cell states reported in these studies. In addition, this review discusses the opportunities and challenges of using single-cell RNA sequencing to study corneal biology in health and disease.
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Affiliation(s)
- Julian A. Arts
- Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6525 GA Nijmegen, The Netherlands; (J.A.A.)
| | - Camille Laberthonnière
- Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6525 GA Nijmegen, The Netherlands; (J.A.A.)
| | - Dulce Lima Cunha
- Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6525 GA Nijmegen, The Netherlands; (J.A.A.)
| | - Huiqing Zhou
- Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6525 GA Nijmegen, The Netherlands; (J.A.A.)
- Department of Human Genetics, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
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11
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Lineage-selective super enhancers mediate core regulatory circuitry during adipogenic and osteogenic differentiation of human mesenchymal stem cells. Cell Death Dis 2022; 13:866. [PMID: 36224171 PMCID: PMC9556616 DOI: 10.1038/s41419-022-05309-3] [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: 07/04/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022]
Abstract
Human mesenchymal stem cells (hMSCs) can be differentiated into osteoblasts and adipocytes. During these processes, super enhancers (SEs) play important roles. Here, we performed comprehensive characterization of the SEs changes associated with adipogenic and osteogenic differentiation of hMSCs, and revealed that SEs changed more dramatically compared with typical enhancers. We identified a set of lineage-selective SEs, whose target genes were enriched with cell type-specific functions. Functional experiments in lineage-selective SEs demonstrated their specific roles in directed differentiation of hMSCs. We also found that some key transcription factors regulated by lineage-selective SEs could form core regulatory circuitry (CRC) to regulate each other's expression and control the hMSCs fate determination. In addition, we found that GWAS SNPs of osteoporosis and obesity were significantly enriched in osteoblasts-selective SEs or adipocytes-selective SEs, respectively. Taken together, our studies unveiled important roles of lineage-selective SEs in hMSCs differentiation into osteoblasts and adipocytes.
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12
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Abstract
Cellular senescence is implicated in a wide range of physiological and pathological conditions throughout an organism's entire lifetime. In particular, it has become evident that senescence plays a causative role in aging and age-associated disorders. This is not due simply to the loss of function of senescent cells. Instead, the substantial alterations of the cellular activities of senescent cells, especially the array of secretory factors, impact the surrounding tissues or even entire organisms. Such non-cell-autonomous functionality is largely coordinated by tissue-specific genes, constituting a cell fate-determining state. Senescence can be viewed as a gain-of-function phenotype or a process of cell identity shift. Cellular functionality or lineage-specific gene expression is tightly linked to the cell type-specific epigenetic landscape, reinforcing the heterogeneity of senescence across cell types. Here, we aim to define the senescence cellular functionality and epigenetic features that may contribute to the gain-of-function phenotype.
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Affiliation(s)
- Ioana Olan
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom; ,
| | - Masashi Narita
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom; ,
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13
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Doyle JJ. Cell types as species: Exploring a metaphor. FRONTIERS IN PLANT SCIENCE 2022; 13:868565. [PMID: 36072310 PMCID: PMC9444152 DOI: 10.3389/fpls.2022.868565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/29/2022] [Indexed: 06/05/2023]
Abstract
The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.
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Affiliation(s)
- Jeff J. Doyle
- Section of Plant Biology and Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
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14
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DiFrisco J, Wagner GP, Love AC. Reframing research on evolutionary novelty and co-option: Character identity mechanisms versus deep homology. Semin Cell Dev Biol 2022; 145:3-12. [PMID: 35400563 DOI: 10.1016/j.semcdb.2022.03.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 01/31/2022] [Accepted: 03/23/2022] [Indexed: 11/27/2022]
Abstract
A central topic in research at the intersection of development and evolution is the origin of novel traits. Despite progress on understanding how developmental mechanisms underlie patterns of diversity in the history of life, the problem of novelty continues to challenge researchers. Here we argue that research on evolutionary novelty and the closely associated phenomenon of co-option can be reframed fruitfully by: (1) specifying a conceptual model of mechanisms that underwrite character identity, (2) providing a richer and more empirically precise notion of co-option that goes beyond common appeals to "deep homology", and (3) attending to the nature of experimental interventions that can determine whether and how the co-option of identity mechanisms can help to explain novel character origins. This reframing has the potential to channel future investigation to make substantive progress on the problem of evolutionary novelty. To illustrate this potential, we apply our reframing to two case studies: treehopper helmets and beetle horns.
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Affiliation(s)
| | - Günter P Wagner
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA; Yale Systems Biology Institute, Yale University, New Haven, CT, USA; Department of Obstetrics, Gynecology and Reproductive Sciences, Yale Medical School, New Haven, CT, USA; Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA
| | - Alan C Love
- Department of Philosophy, University of Minnesota, Minneapolis, MN, USA; Minnesota Center for Philosophy of Sciences, University of Minnesota, Minneapolis, MN, USA.
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15
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16
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Olguín HC. The Gentle Side of the UPS: Ubiquitin-Proteasome System and the Regulation of the Myogenic Program. Front Cell Dev Biol 2022; 9:821839. [PMID: 35127730 PMCID: PMC8811165 DOI: 10.3389/fcell.2021.821839] [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: 11/24/2021] [Accepted: 12/30/2021] [Indexed: 12/12/2022] Open
Abstract
In recent years, the ubiquitin-proteasome system (UPS) has emerged as an important regulator of stem cell function. Here we review recent findings indicating that UPS also plays critical roles in the biology of satellite cells, the muscle stem cell responsible for its maintenance and regeneration. While we focus our attention on the control of key transcriptional regulators of satellite cell function, we briefly discuss early studies suggesting the UPS participates more broadly in the regulation of satellite cell stemness and regenerative capacity.
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17
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Mircea M, Semrau S. How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification. Biochem Soc Trans 2021; 49:2509-2525. [PMID: 34854897 PMCID: PMC8786291 DOI: 10.1042/bst20210135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/06/2021] [Accepted: 11/08/2021] [Indexed: 12/13/2022]
Abstract
On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.
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Affiliation(s)
- Maria Mircea
- Leiden Institute of Physics, Leiden University, Leiden, The Netherlands
| | - Stefan Semrau
- Leiden Institute of Physics, Leiden University, Leiden, The Netherlands
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18
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Dorado G, Gálvez S, Rosales TE, Vásquez VF, Hernández P. Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review. Biomolecules 2021; 11:1111. [PMID: 34439777 PMCID: PMC8393538 DOI: 10.3390/biom11081111] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/12/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023] Open
Abstract
Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.
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Affiliation(s)
- Gabriel Dorado
- Dep. Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, 14071 Córdoba, Spain
| | - Sergio Gálvez
- Dep. Lenguajes y Ciencias de la Computación, Boulevard Louis Pasteur 35, Universidad de Málaga, 29071 Málaga, Spain;
| | - Teresa E. Rosales
- Laboratorio de Arqueobiología, Avda. Universitaria s/n, Universidad Nacional de Trujillo, 13011 Trujillo, Peru;
| | - Víctor F. Vásquez
- Centro de Investigaciones Arqueobiológicas y Paleoecológicas Andinas Arqueobios, Martínez de Companón 430-Bajo 100, Urbanización San Andres, 13088 Trujillo, Peru;
| | - Pilar Hernández
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo s/n, 14080 Córdoba, Spain;
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19
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Almeida N, Chung MWH, Drudi EM, Engquist EN, Hamrud E, Isaacson A, Tsang VSK, Watt FM, Spagnoli FM. Employing core regulatory circuits to define cell identity. EMBO J 2021; 40:e106785. [PMID: 33934382 PMCID: PMC8126924 DOI: 10.15252/embj.2020106785] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 12/12/2022] Open
Abstract
The interplay between extrinsic signaling and downstream gene networks controls the establishment of cell identity during development and its maintenance in adult life. Advances in next-generation sequencing and single-cell technologies have revealed additional layers of complexity in cell identity. Here, we review our current understanding of transcription factor (TF) networks as key determinants of cell identity. We discuss the concept of the core regulatory circuit as a set of TFs and interacting factors that together define the gene expression profile of the cell. We propose the core regulatory circuit as a comprehensive conceptual framework for defining cellular identity and discuss its connections to cell function in different contexts.
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Affiliation(s)
- Nathalia Almeida
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Matthew W H Chung
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Elena M Drudi
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Elise N Engquist
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Eva Hamrud
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Abigail Isaacson
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Victoria S K Tsang
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Fiona M Watt
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Francesca M Spagnoli
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
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