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Moeckel C, Mouratidis I, Chantzi N, Uzun Y, Georgakopoulos-Soares I. Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights. Bioessays 2024; 46:e2300210. [PMID: 38715516 DOI: 10.1002/bies.202300210] [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: 10/31/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
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Affiliation(s)
- Camille Moeckel
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ioannis Mouratidis
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nikol Chantzi
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Yasin Uzun
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
- Department of Pediatrics, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ilias Georgakopoulos-Soares
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
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3
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Zemke NR, Armand EJ, Wang W, Lee S, Zhou J, Li YE, Liu H, Tian W, Nery JR, Castanon RG, Bartlett A, Osteen JK, Li D, Zhuo X, Xu V, Chang L, Dong K, Indralingam HS, Rink JA, Xie Y, Miller M, Krienen FM, Zhang Q, Taskin N, Ting J, Feng G, McCarroll SA, Callaway EM, Wang T, Lein ES, Behrens MM, Ecker JR, Ren B. Conserved and divergent gene regulatory programs of the mammalian neocortex. Nature 2023; 624:390-402. [PMID: 38092918 PMCID: PMC10719095 DOI: 10.1038/s41586-023-06819-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
Divergence of cis-regulatory elements drives species-specific traits1, but how this manifests in the evolution of the neocortex at the molecular and cellular level remains unclear. Here we investigated the gene regulatory programs in the primary motor cortex of human, macaque, marmoset and mouse using single-cell multiomics assays, generating gene expression, chromatin accessibility, DNA methylome and chromosomal conformation profiles from a total of over 200,000 cells. From these data, we show evidence that divergence of transcription factor expression corresponds to species-specific epigenome landscapes. We find that conserved and divergent gene regulatory features are reflected in the evolution of the three-dimensional genome. Transposable elements contribute to nearly 80% of the human-specific candidate cis-regulatory elements in cortical cells. Through machine learning, we develop sequence-based predictors of candidate cis-regulatory elements in different species and demonstrate that the genomic regulatory syntax is highly preserved from rodents to primates. Finally, we show that epigenetic conservation combined with sequence similarity helps to uncover functional cis-regulatory elements and enhances our ability to interpret genetic variants contributing to neurological disease and traits.
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Affiliation(s)
- Nathan R Zemke
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Ethan J Armand
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Wenliang Wang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Seoyeon Lee
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Jingtian Zhou
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Daofeng Li
- Department of Genetics, The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St Louis, MO, USA
| | - Xiaoyu Zhuo
- Department of Genetics, The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St Louis, MO, USA
| | - Vincent Xu
- Department of Genetics, The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St Louis, MO, USA
| | - Lei Chang
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Keyi Dong
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Hannah S Indralingam
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Jonathan A Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Yang Xie
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Michael Miller
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Fenna M Krienen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Genetics, Harvard Medical School, Boston, USA
| | - Qiangge Zhang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Naz Taskin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Guoping Feng
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven A McCarroll
- Department of Genetics, Harvard Medical School, Boston, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Edward M Callaway
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Ting Wang
- Department of Genetics, The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA.
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Genomic Medicine, Moores Cancer Center, School of Medicine, University of California San Diego, La Jolla, CA, USA.
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Friedman RZ, Ramu A, Lichtarge S, Myers CA, Granas DM, Gause M, Corbo JC, Cohen BA, White MA. Active learning of enhancer and silencer regulatory grammar in photoreceptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554146. [PMID: 37662358 PMCID: PMC10473580 DOI: 10.1101/2023.08.21.554146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Cis-regulatory elements (CREs) direct gene expression in health and disease, and models that can accurately predict their activities from DNA sequences are crucial for biomedicine. Deep learning represents one emerging strategy to model the regulatory grammar that relates CRE sequence to function. However, these models require training data on a scale that exceeds the number of CREs in the genome. We address this problem using active machine learning to iteratively train models on multiple rounds of synthetic DNA sequences assayed in live mammalian retinas. During each round of training the model actively selects sequence perturbations to assay, thereby efficiently generating informative training data. We iteratively trained a model that predicts the activities of sequences containing binding motifs for the photoreceptor transcription factor Cone-rod homeobox (CRX) using an order of magnitude less training data than current approaches. The model's internal confidence estimates of its predictions are reliable guides for designing sequences with high activity. The model correctly identified critical sequence differences between active and inactive sequences with nearly identical transcription factor binding sites, and revealed order and spacing preferences for combinations of motifs. Our results establish active learning as an effective method to train accurate deep learning models of cis-regulatory function after exhausting naturally occurring training examples in the genome.
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Affiliation(s)
- Ryan Z. Friedman
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, Saint Louis, MO, 63110
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110
| | - Avinash Ramu
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, Saint Louis, MO, 63110
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110
| | - Sara Lichtarge
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, Saint Louis, MO, 63110
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110
| | - Connie A. Myers
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, 63110
| | - David M. Granas
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, Saint Louis, MO, 63110
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110
| | - Maria Gause
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, 63110
| | - Joseph C. Corbo
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, 63110
| | - Barak A. Cohen
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, Saint Louis, MO, 63110
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110
| | - Michael A. White
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, Saint Louis, MO, 63110
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, 63110
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6
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Gosai SJ, Castro RI, Fuentes N, Butts JC, Kales S, Noche RR, Mouri K, Sabeti PC, Reilly SK, Tewhey R. Machine-guided design of synthetic cell type-specific cis-regulatory elements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552077. [PMID: 37609287 PMCID: PMC10441439 DOI: 10.1101/2023.08.08.552077] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.
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Affiliation(s)
- SJ Gosai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Graduate Program in Biological and Biomedical Science, Boston MA
- Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - RI Castro
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - N Fuentes
- The Jackson Laboratory, Bar Harbor, ME, USA
- Harvard College, Harvard University, Cambridge, MA, USA
| | - JC Butts
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
| | - S Kales
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - RR Noche
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA
- Yale Zebrafish Research Core, Yale School of Medicine, New Haven, CT, USA
| | - K Mouri
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - PC Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - SK Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - R Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
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