The evolution, evolvability and engineering of gene regulatory DNA
Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generaliz...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2023
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| Online Access: | https://hdl.handle.net/1721.1/147055 |
| _version_ | 1826190342364332032 |
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| author | Vaishnav, Eeshit Dhaval de Boer, Carl G Molinet, Jennifer Yassour, Moran Fan, Lin Adiconis, Xian Thompson, Dawn A Levin, Joshua Z Cubillos, Francisco A Regev, Aviv |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Vaishnav, Eeshit Dhaval de Boer, Carl G Molinet, Jennifer Yassour, Moran Fan, Lin Adiconis, Xian Thompson, Dawn A Levin, Joshua Z Cubillos, Francisco A Regev, Aviv |
| author_sort | Vaishnav, Eeshit Dhaval |
| collection | MIT |
| description | Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces4-6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution. |
| first_indexed | 2024-09-23T08:38:32Z |
| format | Article |
| id | mit-1721.1/147055 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:38:32Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1470552023-01-12T03:46:36Z The evolution, evolvability and engineering of gene regulatory DNA Vaishnav, Eeshit Dhaval de Boer, Carl G Molinet, Jennifer Yassour, Moran Fan, Lin Adiconis, Xian Thompson, Dawn A Levin, Joshua Z Cubillos, Francisco A Regev, Aviv Massachusetts Institute of Technology. Department of Biology Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces4-6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution. 2023-01-11T14:45:31Z 2023-01-11T14:45:31Z 2022 2023-01-11T14:40:45Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/147055 Vaishnav, Eeshit Dhaval, de Boer, Carl G, Molinet, Jennifer, Yassour, Moran, Fan, Lin et al. 2022. "The evolution, evolvability and engineering of gene regulatory DNA." Nature, 603 (7901). en 10.1038/S41586-022-04506-6 Nature Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer Science and Business Media LLC PMC |
| spellingShingle | Vaishnav, Eeshit Dhaval de Boer, Carl G Molinet, Jennifer Yassour, Moran Fan, Lin Adiconis, Xian Thompson, Dawn A Levin, Joshua Z Cubillos, Francisco A Regev, Aviv The evolution, evolvability and engineering of gene regulatory DNA |
| title | The evolution, evolvability and engineering of gene regulatory DNA |
| title_full | The evolution, evolvability and engineering of gene regulatory DNA |
| title_fullStr | The evolution, evolvability and engineering of gene regulatory DNA |
| title_full_unstemmed | The evolution, evolvability and engineering of gene regulatory DNA |
| title_short | The evolution, evolvability and engineering of gene regulatory DNA |
| title_sort | evolution evolvability and engineering of gene regulatory dna |
| url | https://hdl.handle.net/1721.1/147055 |
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