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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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2
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Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00469-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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3
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Qin J, Hu Y, Yao JC, Leung RWT, Zhou Y, Qin Y, Wang J. Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data. Brief Bioinform 2021; 22:6347206. [PMID: 34374760 DOI: 10.1093/bib/bbab311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/06/2021] [Accepted: 07/19/2021] [Indexed: 01/09/2023] Open
Abstract
Cell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.
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Affiliation(s)
- Jing Qin
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Yaohua Hu
- Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
| | - Jen-Chih Yao
- Research Center for Interneural Computing, China Medical University, Taichung 40402, Taiwan
| | - Ricky Wai Tak Leung
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Yongqiang Zhou
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Yiming Qin
- Center for Genomic Sciences & School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong
| | - Junwen Wang
- Department of Quantitative Health Sciences and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
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4
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Kaur H, Kumar R, Lathwal A, Raghava GPS. Computational resources for identification of cancer biomarkers from omics data. Brief Funct Genomics 2021; 20:213-222. [PMID: 33788922 DOI: 10.1093/bfgp/elab021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/11/2021] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.
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5
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Huang B, Lu M, Galbraith M, Levine H, Onuchic JN, Jia D. Decoding the mechanisms underlying cell-fate decision-making during stem cell differentiation by random circuit perturbation. J R Soc Interface 2020; 17:20200500. [PMID: 32781932 PMCID: PMC7482558 DOI: 10.1098/rsif.2020.0500] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 07/20/2020] [Indexed: 12/17/2022] Open
Abstract
Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method, random circuit perturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified robust gene states. Among them, four out of the five most probable gene states exhibit gene expression patterns observed in single mouse embryonic cells at 32-cell and 64-cell stages. These gene states can be robustly predicted by the stemness GRN but not by randomized versions of the stemness GRN. Strikingly, we found a hierarchical structure of the GRN with the Oct4/Cdx2 motif functioning as the first decision-making module followed by Gata6/Nanog. We propose that stem cell populations, instead of being viewed as all having a specific cellular state, can be regarded as a heterogeneous mixture including cells in various states. Upon perturbations by external signals, stem cells lose the capacity to access certain cellular states, thereby becoming differentiated. The new gene states and key parameters regulating transitions among gene states proposed by RACIPE can be used to guide experimental strategies to better understand differentiation and design reprogramming. The findings demonstrate that the functions of the stemness GRN is mainly determined by its well-evolved network topology rather than by detailed kinetic parameters.
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Affiliation(s)
- Bin Huang
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
| | - Mingyang Lu
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USA
| | - Madeline Galbraith
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
- Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
- Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Jose N. Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
- Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA
- Department of Chemistry, Rice University, Houston, TX 77005, USA
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
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6
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Hu X, Hu Y, Wu F, Leung RWT, Qin J. Integration of single-cell multi-omics for gene regulatory network inference. Comput Struct Biotechnol J 2020; 18:1925-1938. [PMID: 32774787 PMCID: PMC7385034 DOI: 10.1016/j.csbj.2020.06.033] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 06/17/2020] [Accepted: 06/20/2020] [Indexed: 12/20/2022] Open
Abstract
The advancement of single-cell sequencing technology in recent years has provided an opportunity to reconstruct gene regulatory networks (GRNs) with the data from thousands of single cells in one sample. This uncovers regulatory interactions in cells and speeds up the discoveries of regulatory mechanisms in diseases and biological processes. Therefore, more methods have been proposed to reconstruct GRNs using single-cell sequencing data. In this review, we introduce technologies for sequencing single-cell genome, transcriptome, and epigenome. At the same time, we present an overview of current GRN reconstruction strategies utilizing different single-cell sequencing data. Bioinformatics tools were grouped by their input data type and mathematical principles for reader's convenience, and the fundamental mathematics inherent in each group will be discussed. Furthermore, the adaptabilities and limitations of these different methods will also be summarized and compared, with the hope to facilitate researchers recognizing the most suitable tools for them.
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Affiliation(s)
- Xinlin Hu
- Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
| | - Yaohua Hu
- Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China
| | - Fanjie Wu
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Ricky Wai Tak Leung
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Jing Qin
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
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7
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Chimusa ER, Dalvie S, Dandara C, Wonkam A, Mazandu GK. Post genome-wide association analysis: dissecting computational pathway/network-based approaches. Brief Bioinform 2020; 20:690-700. [PMID: 29701762 DOI: 10.1093/bib/bby035] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 04/04/2018] [Indexed: 02/02/2023] Open
Abstract
Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.
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Affiliation(s)
- Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Level 3, Wernher and Beit North, Private Bag, Rondebosch, 7700, Anzio road, Observatory Cape Town, South Africa
| | - Shareefa Dalvie
- Department of Psychiatry and Mental Health, University of Cape Town, Observatory, 7925, Cape Town, South Africa
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa; African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa and Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Anzio Road, Observatory, 7925, Cape Town, South Africa
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8
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Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM. Gene regulatory network inference resources: A practical overview. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194430. [PMID: 31678629 DOI: 10.1016/j.bbagrm.2019.194430] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 02/08/2023]
Abstract
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Daniele Mercatelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Laura Scalambra
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Luca Triboli
- Centre for Integrative Biology (CIBIO), University of Trento, Italy
| | - Forest Ray
- Department of Systems Biology, Columbia University Medical Center, New York, NY, United States
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
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Lukoseviciute M, Gavriouchkina D, Williams RM, Hochgreb-Hagele T, Senanayake U, Chong-Morrison V, Thongjuea S, Repapi E, Mead A, Sauka-Spengler T. From Pioneer to Repressor: Bimodal foxd3 Activity Dynamically Remodels Neural Crest Regulatory Landscape In Vivo. Dev Cell 2019; 47:608-628.e6. [PMID: 30513303 PMCID: PMC6286384 DOI: 10.1016/j.devcel.2018.11.009] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 08/15/2018] [Accepted: 10/31/2018] [Indexed: 02/06/2023]
Abstract
The neural crest (NC) is a transient embryonic stem cell-like population characterized by its multipotency and broad developmental potential. Here, we perform NC-specific transcriptional and epigenomic profiling of foxd3-mutant cells in vivo to define the gene regulatory circuits controlling NC specification. Together with global binding analysis obtained by foxd3 biotin-ChIP and single cell profiles of foxd3-expressing premigratory NC, our analysis shows that, during early steps of NC formation, foxd3 acts globally as a pioneer factor to prime the onset of genes regulating NC specification and migration by re-arranging the chromatin landscape, opening cis-regulatory elements and reshuffling nucleosomes. Strikingly, foxd3 then gradually switches from an activator to its well-described role as a transcriptional repressor and potentially uses differential partners for each role. Taken together, these results demonstrate that foxd3 acts bimodally in the neural crest as a switch from “permissive” to “repressive” nucleosome and chromatin organization to maintain multipotency and define cell fates. FoxD3 primes neural crest specification by modulating distal enhancers FoxD3 represses a number of neural crest migration and differentiation genes In neural crest, FoxD3 acts to switch chromatin from “permissive” to “repressive” Distinctive gene regulatory mechanisms underlie the bimodal action of FoxD3
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Affiliation(s)
- Martyna Lukoseviciute
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Daria Gavriouchkina
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Ruth M Williams
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Tatiana Hochgreb-Hagele
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Upeka Senanayake
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Vanessa Chong-Morrison
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Supat Thongjuea
- Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Emmanouela Repapi
- MRC WIMM Centre for Computational Biology Research Group, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Adam Mead
- Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Tatjana Sauka-Spengler
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK.
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Wang B, Wang P, Parobchak N, Treff N, Tao X, Wang J, Rosen T. Integrated RNA-seq and ChIP-seq analysis reveals a feed-forward loop regulating H3K9ac and key labor drivers in human placenta. Placenta 2019; 76:40-50. [PMID: 30642670 DOI: 10.1016/j.placenta.2019.01.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/06/2019] [Accepted: 01/08/2019] [Indexed: 12/23/2022]
Abstract
BACKGROUND Chromatin alterations are important mediators of gene expression changes. We have recently shown that activated non-canonical NF-κB signaling (RelB/p52) recruits histone acetyltransferase CBP and deacetylase HDAC1 to selectively acetylate H3K9 (H3K9ac) to induce expression of corticotropin-releasing hormone (CRH) and prostaglandin-endoperoxide synthase-2 (PTGS2) in the human placenta. Both of these genes play a role in initiating parturition in human pregnancy. METHODS We performed chromatin immunoprecipitation followed by gene sequencing (ChIP-seq) in primary term human cytotrophoblast (CTB) with use of antibodies to RelB, CBP, HDAC1 and H3K9ac. We further associated these chromatin alterations with gene expression changes from mid-trimester to term in CTB by RNA sequencing (RNA-seq). RESULTS We detected a genome-wide differential gene enrichment between mid-trimester and term human placenta. Pathway analysis identified that cytokine-cytokine receptor interaction, NF-κB, and TNF are the leading pathways enriched in term placenta and associated with these chromatin alterations. DISCUSSIONS Our analysis has provided the first-time characterization of the key players of human placental origin with molecular changes resulting from chromatin modifications, which could drive human labor.
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Affiliation(s)
- Bingbing Wang
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Division of Maternal-Fetal Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA.
| | - Panwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Nataliya Parobchak
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Division of Maternal-Fetal Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA
| | - Nathan Treff
- Genomic Prediction Inc, North Brunswick, NJ, 08902, USA
| | - Xin Tao
- Foundation for Embryonic Competence, Basking Ridge, NJ, 07920, USA
| | - Junwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Todd Rosen
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Division of Maternal-Fetal Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA.
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11
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An integrative method to decode regulatory logics in gene transcription. Nat Commun 2017; 8:1044. [PMID: 29051499 PMCID: PMC5715098 DOI: 10.1038/s41467-017-01193-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 08/25/2017] [Indexed: 12/27/2022] Open
Abstract
Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF–TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems. Existing transcriptional regulatory networks models fall short of deciphering the cooperation between multiple transcription factors on dynamic gene expression. Here the authors develop an integrative method that combines gene expression and transcription factor-DNA binding data to decode transcription regulatory logics.
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12
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Inukai S, Kock KH, Bulyk ML. Transcription factor-DNA binding: beyond binding site motifs. Curr Opin Genet Dev 2017; 43:110-119. [PMID: 28359978 PMCID: PMC5447501 DOI: 10.1016/j.gde.2017.02.007] [Citation(s) in RCA: 200] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 02/02/2017] [Accepted: 02/07/2017] [Indexed: 12/12/2022]
Abstract
Sequence-specific transcription factors (TFs) regulate gene expression by binding to cis-regulatory elements in promoter and enhancer DNA. While studies of TF-DNA binding have focused on TFs' intrinsic preferences for primary nucleotide sequence motifs, recent studies have elucidated additional layers of complexity that modulate TF-DNA binding. In this review, we discuss technological developments for identifying TF binding preferences and highlight recent discoveries that elaborate how TF interactions, local DNA structure, and genomic features influence TF-DNA binding. We highlight novel approaches for characterizing functional binding site motifs that promise to inform our understanding of how TF binding controls gene expression and ultimately contributes to phenotype.
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Affiliation(s)
- Sachi Inukai
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Kian Hong Kock
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA 02138, USA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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13
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Qin J, Yan B, Hu Y, Wang P, Wang J. Applications of integrative OMICs approaches to gene regulation studies. QUANTITATIVE BIOLOGY 2016. [DOI: 10.1007/s40484-016-0085-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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14
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Liu B, Zhou C, Li G, Zhang H, Zeng E, Liu Q, Ma Q. Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses. Sci Rep 2016; 6:23030. [PMID: 26975728 PMCID: PMC4792141 DOI: 10.1038/srep23030] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 02/22/2016] [Indexed: 12/18/2022] Open
Abstract
Regulons are the basic units of the response system in a bacterial cell, and each consists of a set of transcriptionally co-regulated operons. Regulon elucidation is the basis for studying the bacterial global transcriptional regulation network. In this study, we designed a novel co-regulation score between a pair of operons based on accurate operon identification and cis regulatory motif analyses, which can capture their co-regulation relationship much better than other scores. Taking full advantage of this discovery, we developed a new computational framework and built a novel graph model for regulon prediction. This model integrates the motif comparison and clustering and makes the regulon prediction problem substantially more solvable and accurate. To evaluate our prediction, a regulon coverage score was designed based on the documented regulons and their overlap with our prediction; and a modified Fisher Exact test was implemented to measure how well our predictions match the co-expressed modules derived from E. coli microarray gene-expression datasets collected under 466 conditions. The results indicate that our program consistently performed better than others in terms of the prediction accuracy. This suggests that our algorithms substantially improve the state-of-the-art, leading to a computational capability to reliably predict regulons for any bacteria.
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Affiliation(s)
- Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Chuan Zhou
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Guojun Li
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Hanyuan Zhang
- Systems Biology and Biomedical Informatics (SBBI) Laboratory University of Nebraska-Lincoln, Lincoln, NE 68588-0115, USA
| | - Erliang Zeng
- Department of Biology, University of South Dakota, Vermillion, SD 57069, USA.,Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA.,BioSNTR, Brookings, SD, USA
| | - Qi Liu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Qin Ma
- Department of Plant Science, South Dakota State University, Brookings, SD, 57006, USA.,BioSNTR, Brookings, SD, USA
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