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Andreani V, South EJ, Dunlop MJ. Generating information-dense promoter sequences with optimal string packing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565124. [PMID: 37961203 PMCID: PMC10635063 DOI: 10.1101/2023.11.01.565124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts. Author Summary The way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.
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DaSilva LF, Senan S, Patel ZM, Janardhan Reddy A, Gabbita S, Nussbaum Z, Valdez Córdova CM, Wenteler A, Weber N, Tunjic TM, Ahmad Khan T, Li Z, Smith C, Bejan M, Karmel Louis L, Cornejo P, Connell W, Wong ES, Meuleman W, Pinello L. DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.578352. [PMID: 38352499 PMCID: PMC10862870 DOI: 10.1101/2024.02.01.578352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
The challenge of systematically modifying and optimizing regulatory elements for precise gene expression control is central to modern genomics and synthetic biology. Advancements in generative AI have paved the way for designing synthetic sequences with the aim of safely and accurately modulating gene expression. We leverage diffusion models to design context-specific DNA regulatory sequences, which hold significant potential toward enabling novel therapeutic applications requiring precise modulation of gene expression. Our framework uses a cell type-specific diffusion model to generate synthetic 200 bp regulatory elements based on chromatin accessibility across different cell types. We evaluate the generated sequences based on key metrics to ensure they retain properties of endogenous sequences: transcription factor binding site composition, potential for cell type-specific chromatin accessibility, and capacity for sequences generated by DNA diffusion to activate gene expression in different cell contexts using state-of-the-art prediction models. Our results demonstrate the ability to robustly generate DNA sequences with cell type-specific regulatory potential. DNA-Diffusion paves the way for revolutionizing a regulatory modulation approach to mammalian synthetic biology and precision gene therapy.
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Affiliation(s)
- Lucas Ferreira DaSilva
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Simon Senan
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Zain Munir Patel
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Aniketh Janardhan Reddy
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Sameer Gabbita
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | | | | | | | - Zelun Li
- Victor Chang Cardiac Institute, Darlinghurst, New South Wales, Australia
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, UNSW Sydney, Sydney, Australia
| | - Cameron Smith
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Lithin Karmel Louis
- Victor Chang Cardiac Institute, Darlinghurst, New South Wales, Australia
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, UNSW Sydney, Sydney, Australia
| | - Paola Cornejo
- Victor Chang Cardiac Institute, Darlinghurst, New South Wales, Australia
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, UNSW Sydney, Sydney, Australia
| | | | - Emily S. Wong
- Victor Chang Cardiac Institute, Darlinghurst, New South Wales, Australia
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, UNSW Sydney, Sydney, Australia
| | - Wouter Meuleman
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Luca Pinello
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
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de Almeida BP, Schaub C, Pagani M, Secchia S, Furlong EEM, Stark A. Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo. Nature 2024; 626:207-211. [PMID: 38086418 PMCID: PMC10830412 DOI: 10.1038/s41586-023-06905-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/28/2023] [Indexed: 01/19/2024]
Abstract
Enhancers control gene expression and have crucial roles in development and homeostasis1-3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.
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Affiliation(s)
- Bernardo P de Almeida
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- InstaDeep, Paris, France
| | - Christoph Schaub
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Michaela Pagani
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Stefano Secchia
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Eileen E M Furlong
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Alexander Stark
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.
- Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria.
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Martyn GE, Montgomery MT, Jones H, Guo K, Doughty BR, Linder J, Chen Z, Cochran K, Lawrence KA, Munson G, Pampari A, Fulco CP, Kelley DR, Lander ES, Kundaje A, Engreitz JM. Rewriting regulatory DNA to dissect and reprogram gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572268. [PMID: 38187584 PMCID: PMC10769263 DOI: 10.1101/2023.12.20.572268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.
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Affiliation(s)
- Gabriella E Martyn
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Michael T Montgomery
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Hank Jones
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Katherine Guo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Benjamin R Doughty
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ziwei Chen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Kelly Cochran
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Kathryn A Lawrence
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Glen Munson
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anusri Pampari
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Present Address: Sanofi, Cambridge, MA, USA
| | | | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jesse M Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
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Gjoni K, Pollard KS. SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565556. [PMID: 37961123 PMCID: PMC10635135 DOI: 10.1101/2023.11.03.565556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Computationally editing genome sequences is a common bioinformatics task, but current approaches have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing in silico mutagenesis. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences.
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Affiliation(s)
- Ketrin Gjoni
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco, CA 94158, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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