The biochemical basis of microRNA targeting efficacy
© 2019 American Association for the Advancement of Science. All rights reserved. MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurement...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/136496 |
| _version_ | 1826213270487302144 |
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| author | McGeary, Sean E Lin, Kathy S Shi, Charlie Y Pham, Thy M Bisaria, Namita Kelley, Gina M Bartel, David P |
| author2 | Howard Hughes Medical Institute |
| author_facet | Howard Hughes Medical Institute McGeary, Sean E Lin, Kathy S Shi, Charlie Y Pham, Thy M Bisaria, Namita Kelley, Gina M Bartel, David P |
| author_sort | McGeary, Sean E |
| collection | MIT |
| description | © 2019 American Association for the Advancement of Science. All rights reserved. MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks. |
| first_indexed | 2024-09-23T15:46:40Z |
| format | Article |
| id | mit-1721.1/136496 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:46:40Z |
| publishDate | 2021 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | dspace |
| spelling | mit-1721.1/1364962023-10-06T20:18:35Z The biochemical basis of microRNA targeting efficacy McGeary, Sean E Lin, Kathy S Shi, Charlie Y Pham, Thy M Bisaria, Namita Kelley, Gina M Bartel, David P Howard Hughes Medical Institute Whitehead Institute for Biomedical Research Massachusetts Institute of Technology. Department of Biology Massachusetts Institute of Technology. Computational and Systems Biology Program © 2019 American Association for the Advancement of Science. All rights reserved. MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks. 2021-10-27T20:35:40Z 2021-10-27T20:35:40Z 2019 2021-07-14T13:46:57Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136496 en 10.1126/SCIENCE.AAV1741 Science Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Association for the Advancement of Science (AAAS) PMC |
| spellingShingle | McGeary, Sean E Lin, Kathy S Shi, Charlie Y Pham, Thy M Bisaria, Namita Kelley, Gina M Bartel, David P The biochemical basis of microRNA targeting efficacy |
| title | The biochemical basis of microRNA targeting efficacy |
| title_full | The biochemical basis of microRNA targeting efficacy |
| title_fullStr | The biochemical basis of microRNA targeting efficacy |
| title_full_unstemmed | The biochemical basis of microRNA targeting efficacy |
| title_short | The biochemical basis of microRNA targeting efficacy |
| title_sort | biochemical basis of microrna targeting efficacy |
| url | https://hdl.handle.net/1721.1/136496 |
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