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Owen DM, Kwon M, Huang X, Nagari A, Nandu T, Kraus WL. Genome-wide identification of transcriptional enhancers during human placental development and association with function, differentiation, and disease†. Biol Reprod 2023; 109:965-981. [PMID: 37694817 PMCID: PMC10724456 DOI: 10.1093/biolre/ioad119] [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: 01/28/2023] [Revised: 08/07/2023] [Accepted: 09/09/2023] [Indexed: 09/12/2023] Open
Abstract
The placenta is a dynamic organ that must perform a remarkable variety of functions during its relatively short existence in order to support a developing fetus. These functions include nutrient delivery, gas exchange, waste removal, hormone production, and immune barrier protection. Proper placenta development and function are critical for healthy pregnancy outcomes, but the underlying genomic regulatory events that control this process remain largely unknown. We hypothesized that mapping sites of transcriptional enhancer activity and associated changes in gene expression across gestation in human placenta tissue would identify genomic loci and predicted transcription factor activity related to critical placenta functions. We used a suite of genomic assays [i.e., RNA-sequencing (RNA-seq), Precision run-on-sequencing (PRO-seq), and Chromatin immunoprecipitation-sequencing (ChIP-seq)] and computational pipelines to identify a set of >20 000 enhancers that are active at various time points in gestation. Changes in the activity of these enhancers correlate with changes in gene expression. In addition, some of these enhancers encode risk for adverse pregnancy outcomes. We further show that integrating enhancer activity, transcription factor motif analysis, and transcription factor expression can identify distinct sets of transcription factors predicted to be more active either in early pregnancy or at term. Knockdown of selected identified transcription factors in a trophoblast stem cell culture model altered the expression of key placental marker genes. These observations provide a framework for future mechanistic studies of individual enhancer-transcription factor-target gene interactions and have the potential to inform genetic risk prediction for adverse pregnancy outcomes.
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Affiliation(s)
- David M Owen
- Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of General Obstetrics and Gynecology, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Minjung Kwon
- Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xuan Huang
- Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anusha Nagari
- Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tulip Nandu
- Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - W Lee Kraus
- Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Regner MJ, Wisniewska K, Garcia-Recio S, Thennavan A, Mendez-Giraldez R, Malladi VS, Hawkins G, Parker JS, Perou CM, Bae-Jump VL, Franco HL. A multi-omic single-cell landscape of human gynecologic malignancies. Mol Cell 2021; 81:4924-4941.e10. [PMID: 34739872 PMCID: PMC8642316 DOI: 10.1016/j.molcel.2021.10.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 08/05/2021] [Accepted: 10/13/2021] [Indexed: 01/05/2023]
Abstract
Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.
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Affiliation(s)
- Matthew J. Regner
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Bioinformatics and Computational Biology Graduate Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,These authors contributed equally
| | - Kamila Wisniewska
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,These authors contributed equally
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Aatish Thennavan
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Oral and Craniofacial Biomedicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Raul Mendez-Giraldez
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Venkat S. Malladi
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Gabrielle Hawkins
- Division of Gynecology Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Joel S. Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Victoria L. Bae-Jump
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Division of Gynecology Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hector L. Franco
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Bioinformatics and Computational Biology Graduate Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Lead contact.,Correspondence:
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Nystrom SL, McKay DJ. Memes: A motif analysis environment in R using tools from the MEME Suite. PLoS Comput Biol 2021; 17:e1008991. [PMID: 34570758 PMCID: PMC8496816 DOI: 10.1371/journal.pcbi.1008991] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/07/2021] [Accepted: 09/10/2021] [Indexed: 11/18/2022] Open
Abstract
Identification of biopolymer motifs represents a key step in the analysis of biological sequences. The MEME Suite is a widely used toolkit for comprehensive analysis of biopolymer motifs; however, these tools are poorly integrated within popular analysis frameworks like the R/Bioconductor project, creating barriers to their use. Here we present memes, an R package that provides a seamless R interface to a selection of popular MEME Suite tools. memes provides a novel “data aware” interface to these tools, enabling rapid and complex discriminative motif analysis workflows. In addition to interfacing with popular MEME Suite tools, memes leverages existing R/Bioconductor data structures to store the multidimensional data returned by MEME Suite tools for rapid data access and manipulation. Finally, memes provides data visualization capabilities to facilitate communication of results. memes is available as a Bioconductor package at https://bioconductor.org/packages/memes, and the source code can be found at github.com/snystrom/memes. Biologically active molecules such as DNA, RNA, and proteins are polymers composed of repeated subunits. For example, nucleotides are the subunits of DNA and RNA, and amino acids are the subunits of proteins. Functional properties of biopolymers are determined by short, recurring stretches of subunits known as motifs. Motifs can serve as binding sites between molecules, they can influence the structure of molecules, and they can contribute to enzymatic activities. For these reasons, motif analysis has become a key step in determining the function of biopolymers and in elucidating their roles in biological networks. The MEME Suite is a widely used compilation of tools used for identifying and analyzing motifs found in biological sequences. Here, we describe a new piece of software named “memes” that connects MEME Suite tools to R, the statistical analysis environment. By providing an interface between the MEME Suite and R, memes allows for improved motif analysis workflows and easy access to a wide range of existing data visualization tools, further expanding the utility of MEME Suite tools.
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Affiliation(s)
- Spencer L. Nystrom
- Curriculum in Genetics and Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Integrative Program for Biological and Genome Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Daniel J. McKay
- Department of Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Integrative Program for Biological and Genome Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- * E-mail:
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