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Perez AA, Goronzy IN, Blanco MR, Guo JK, Guttman M. ChIP-DIP: A multiplexed method for mapping hundreds of proteins to DNA uncovers diverse regulatory elements controlling gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.14.571730. [PMID: 38187704 PMCID: PMC10769186 DOI: 10.1101/2023.12.14.571730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Gene expression is controlled by the dynamic localization of thousands of distinct regulatory proteins to precise regions of DNA. Understanding this cell-type specific process has been a goal of molecular biology for decades yet remains challenging because most current DNA-protein mapping methods study one protein at a time. To overcome this, we developed ChIP-DIP (ChIP Done In Parallel), a split-pool based method that enables simultaneous, genome-wide mapping of hundreds of diverse regulatory proteins in a single experiment. We demonstrate that ChIP-DIP generates highly accurate maps for all classes of DNA-associated proteins, including histone modifications, chromatin regulators, transcription factors, and RNA Polymerases. Using these data, we explore quantitative combinations of protein localization on genomic DNA to define distinct classes of regulatory elements and their functional activity. Our data demonstrate that ChIP-DIP enables the generation of 'consortium level', context-specific protein localization maps within any molecular biology lab.
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2
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Bafna M, Li H, Zhang X. CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics. Bioinformatics 2023; 39:i484-i493. [PMID: 37387180 DOI: 10.1093/bioinformatics/btad269] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. RESULTS We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks. AVAILABILITY AND IMPLEMENTATION The source code and data is available at https://github.com/MihirBafna/CLARIFY.
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Affiliation(s)
- Mihir Bafna
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, United States
| | - Hechen Li
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, United States
| | - Xiuwei Zhang
- School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, United States
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3
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Computational approaches to understand transcription regulation in development. Biochem Soc Trans 2023; 51:1-12. [PMID: 36695505 PMCID: PMC9988001 DOI: 10.1042/bst20210145] [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: 10/28/2022] [Revised: 01/07/2023] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.
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4
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Tang J, Xu Q, Tang K, Ye X, Cao Z, Zou M, Zeng J, Guan X, Han J, Wang Y, Yang L, Lin Y, Jiang K, Chen X, Zhao Y, Tian D, Li C, Shen W, Du X. Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome. Front Immunol 2023; 13:1048774. [PMID: 36713410 PMCID: PMC9878565 DOI: 10.3389/fimmu.2022.1048774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
Introduction Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza. Methods Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized. Results We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79. Discussion The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.
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Affiliation(s)
- Jing Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Qiumei Xu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,Guangzhou Eighth People’s Hospital, Guangzhou Medical University, Guangzhou, China
| | - Kang Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Ye
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zicheng Cao
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,School of Public Health, Shantou University, Shantou, China
| | - Min Zou
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xinyan Guan
- Department of Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Jinglin Han
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Yihan Wang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Lan Yang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Yishan Lin
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Kaiao Jiang
- Palos Verdes Peninsula High School, Rancho Palos Verdes, CA, United States
| | - Xiaoliang Chen
- Department of Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Yang Zhao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Dechao Tian
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Chunwei Li
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wei Shen
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China,*Correspondence: Xiangjun Du, ; Wei Shen,
| | - Xiangjun Du
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China,*Correspondence: Xiangjun Du, ; Wei Shen,
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5
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NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity. Genome Biol 2022; 23:270. [PMID: 36575445 PMCID: PMC9793520 DOI: 10.1186/s13059-022-02835-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
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6
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Iyer NR, Ashton RS. Bioengineering the human spinal cord. Front Cell Dev Biol 2022; 10:942742. [PMID: 36092702 PMCID: PMC9458954 DOI: 10.3389/fcell.2022.942742] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/01/2022] [Indexed: 12/04/2022] Open
Abstract
Three dimensional, self-assembled organoids that recapitulate key developmental and organizational events during embryogenesis have proven transformative for the study of human central nervous system (CNS) development, evolution, and disease pathology. Brain organoids have predominated the field, but human pluripotent stem cell (hPSC)-derived models of the spinal cord are on the rise. This has required piecing together the complex interactions between rostrocaudal patterning, which specifies axial diversity, and dorsoventral patterning, which establishes locomotor and somatosensory phenotypes. Here, we review how recent insights into neurodevelopmental biology have driven advancements in spinal organoid research, generating experimental models that have the potential to deepen our understanding of neural circuit development, central pattern generation (CPG), and neurodegenerative disease along the body axis. In addition, we discuss the application of bioengineering strategies to drive spinal tissue morphogenesis in vitro, current limitations, and future perspectives on these emerging model systems.
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Affiliation(s)
- Nisha R. Iyer
- Department of Biomedical Engineering, Tufts University, Medford, MA, United States
- Wisconsin Institute for Discovery, University of Wisconsin—Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin—Madison, Madison, WI, United States
| | - Randolph S. Ashton
- Wisconsin Institute for Discovery, University of Wisconsin—Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin—Madison, Madison, WI, United States
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7
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Chen L, Wu CT, Lin CH, Dai R, Liu C, Clarke R, Yu G, Van Eyk JE, Herrington DM, Wang Y. swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution. Bioinformatics 2022; 38:1403-1410. [PMID: 34904628 PMCID: PMC8826012 DOI: 10.1093/bioinformatics/btab839] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/30/2021] [Accepted: 12/10/2021] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. RESULTS We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. AVAILABILITY AND IMPLEMENTATION The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lulu Chen
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Chiung-Ting Wu
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Chia-Hsiang Lin
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Robert Clarke
- The Hormel Institute, University of Minnesota, Austin, MN 55912, USA
| | - Guoqiang Yu
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - David M Herrington
- Department of Internal Medicine, Wake Forest University, Winston-Salem, NC 27157, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
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8
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Kashima M, Shida Y, Yamashiro T, Hirata H, Kurosaka H. Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq. FRONTIERS IN BIOINFORMATICS 2021; 1:777299. [PMID: 36303726 PMCID: PMC9580923 DOI: 10.3389/fbinf.2021.777299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.
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Affiliation(s)
- Makoto Kashima
- College of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan
- *Correspondence: Makoto Kashima,
| | - Yuki Shida
- Department of Orthodontics and Dentofacial Orthopedics, Osaka University, Suita, Japan
| | - Takashi Yamashiro
- Department of Orthodontics and Dentofacial Orthopedics, Osaka University, Suita, Japan
| | - Hiromi Hirata
- College of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan
| | - Hiroshi Kurosaka
- Department of Orthodontics and Dentofacial Orthopedics, Osaka University, Suita, Japan
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9
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Viksna J, Melkus G, Celms E, Čerāns K, Freivalds K, Kikusts P, Lace L, Opmanis M, Rituma D, Rucevskis P. Topological structure analysis of chromatin interaction networks. BMC Bioinformatics 2019; 20:618. [PMID: 31881819 PMCID: PMC6933681 DOI: 10.1186/s12859-019-3237-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 11/14/2019] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks. RESULTS It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, such evidence was based on manual analysis and was limited. Here we present a novel method for analysis of chromatin interaction networks aimed towards identifying characteristic topological features of interaction graphs and confirming their potential significance in chromatin architecture. Our method automatically identifies all connected components with an assigned significance score above a given threshold. These components can be subjected afterwards to different assessment methods for their biological role and/or significance. The method was applied to the largest PCHi-C data set available to date that contains interactions for 17 haematopoietic cell types. The results demonstrate strong evidence of well-pronounced component structure of chromatin interaction networks and provide some characterisation of this component structure. We also performed an indicative assessment of potential biological significance of identified network components with the results confirming that the network components can be related to specific biological functionality. CONCLUSIONS The obtained results show that the topological structure of chromatin interaction networks can be well described in terms of isolated connected components of the network and that formation of these components can be often explained by biological features of functionally related gene modules. The presented method allows automatic identification of all such components and evaluation of their significance in PCHi-C dataset for 17 haematopoietic cell types. The method can be adapted for exploration of other chromatin interaction data sets that include information about sufficiently large number of different cell types, and, in principle, also for analysis of other kinds of cell type-specific networks.
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Affiliation(s)
- Juris Viksna
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia. .,Faculty of Computing, University of Latvia, Rainis boulevard 19, Riga, LV-1586, Latvia.
| | - Gatis Melkus
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia
| | - Edgars Celms
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia.,Faculty of Computing, University of Latvia, Rainis boulevard 19, Riga, LV-1586, Latvia
| | - Kārlis Čerāns
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia.,Faculty of Computing, University of Latvia, Rainis boulevard 19, Riga, LV-1586, Latvia
| | - Karlis Freivalds
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia.,Faculty of Computing, University of Latvia, Rainis boulevard 19, Riga, LV-1586, Latvia
| | - Paulis Kikusts
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia
| | - Lelde Lace
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia.,Faculty of Computing, University of Latvia, Rainis boulevard 19, Riga, LV-1586, Latvia
| | - Mārtiņš Opmanis
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia
| | - Darta Rituma
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia.,Faculty of Computing, University of Latvia, Rainis boulevard 19, Riga, LV-1586, Latvia
| | - Peteris Rucevskis
- Institute of Mathematics and Computer Science, University of Latvia, Rainis boulevard 29, Riga, LV-1459, Latvia
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10
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Chasman D, Iyer N, Fotuhi Siahpirani A, Estevez Silva M, Lippmann E, McIntosh B, Probasco MD, Jiang P, Stewart R, Thomson JA, Ashton RS, Roy S. Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development. Cell Syst 2019; 9:167-186.e12. [PMID: 31302154 DOI: 10.1016/j.cels.2019.05.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 05/05/2019] [Accepted: 05/30/2019] [Indexed: 12/19/2022]
Abstract
Neuroepithelial stem cells (NSC) from different anatomical regions of the embryonic neural tube's rostrocaudal axis can differentiate into diverse central nervous system tissues, but the transcriptional regulatory networks governing these processes are incompletely understood. Here, we measure region-specific NSC gene expression along the rostrocaudal axis in a human pluripotent stem cell model of early central nervous system development over a 72-h time course, spanning the hindbrain to cervical spinal cord. We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically and predict their upstream regulators. We identify known regulators of patterning and neural development, including the HOX genes, and predict a direct regulatory connection between the transcription factor POU3F2 and target gene STMN2. We demonstrate that POU3F2 is required for expression of STMN2, suggesting that this regulatory connection is important for region specificity of NSCs.
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Affiliation(s)
- Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Nisha Iyer
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Maria Estevez Silva
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ethan Lippmann
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Brian McIntosh
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Mitchell D Probasco
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Peng Jiang
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Ron Stewart
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - James A Thomson
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Randolph S Ashton
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.
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11
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Assi SA, Imperato MR, Coleman DJL, Pickin A, Potluri S, Ptasinska A, Chin PS, Blair H, Cauchy P, James SR, Zacarias-Cabeza J, Gilding LN, Beggs A, Clokie S, Loke JC, Jenkin P, Uddin A, Delwel R, Richards SJ, Raghavan M, Griffiths MJ, Heidenreich O, Cockerill PN, Bonifer C. Subtype-specific regulatory network rewiring in acute myeloid leukemia. Nat Genet 2019; 51:151-162. [PMID: 30420649 PMCID: PMC6330064 DOI: 10.1038/s41588-018-0270-1] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 10/02/2018] [Indexed: 12/30/2022]
Abstract
Acute myeloid leukemia (AML) is a heterogeneous disease caused by a variety of alterations in transcription factors, epigenetic regulators and signaling molecules. To determine how different mutant regulators establish AML subtype-specific transcriptional networks, we performed a comprehensive global analysis of cis-regulatory element activity and interaction, transcription factor occupancy and gene expression patterns in purified leukemic blast cells. Here, we focused on specific subgroups of subjects carrying mutations in genes encoding transcription factors (RUNX1, CEBPα), signaling molecules (FTL3-ITD, RAS) and the nuclear protein NPM1). Integrated analysis of these data demonstrates that each mutant regulator establishes a specific transcriptional and signaling network unrelated to that seen in normal cells, sustaining the expression of unique sets of genes required for AML growth and maintenance.
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Affiliation(s)
- Salam A Assi
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | | | - Daniel J L Coleman
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Anna Pickin
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Sandeep Potluri
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Anetta Ptasinska
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Paulynn Suyin Chin
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Helen Blair
- Northern Institute for Cancer Research, University of Newcastle, Newcastle, UK
| | - Pierre Cauchy
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Sally R James
- Section of Experimental Haematology, Leeds Institute for Molecular Medicine, University of Leeds, Leeds, UK
| | | | - L Niall Gilding
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Andrew Beggs
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Sam Clokie
- West Midlands Regional Genetics Laboratory, Birmingham Women's NHS Foundation Trust, Birmingham, UK
| | - Justin C Loke
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Phil Jenkin
- CMT Laboratory NHS Blood & Transplant, Edgbaston, Birmingham, UK
| | - Ash Uddin
- CMT Laboratory NHS Blood & Transplant, Edgbaston, Birmingham, UK
| | - Ruud Delwel
- Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Oncode Institute, Erasmus MC, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Stephen J Richards
- Haematological Malignancy Diagnostic Service, St. James's University Hospital, Leeds, UK
| | - Manoj Raghavan
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Centre for Clinical Haematology, Queen Elizabeth Hospital, Birmingham, UK
| | - Michael J Griffiths
- West Midlands Regional Genetics Laboratory, Birmingham Women's NHS Foundation Trust, Birmingham, UK
| | - Olaf Heidenreich
- Northern Institute for Cancer Research, University of Newcastle, Newcastle, UK
- Princess Maxima Centrum for Pediatric Oncology, Utrecht, The Netherlands
| | - Peter N Cockerill
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
| | - Constanze Bonifer
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
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