The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes
Open accessAbstract
RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited. Here we repurpose a high-throughput sequencing instrument to quantitatively measure binding and dissociation of a fluorescently labeled protein to >107 RNA targets generated on a flow cell surface by in situ transcription and intermolecular tethering of RNA to DNA. Studying the MS2 coat protein, we decompose the binding energy contributions from primary and secondary RNA structure, and observe that differences in affinity are often driven by sequence-specific changes in both association and dissociation rates. By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories. Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.
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Cites
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
Self-citationAccurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684
Long Noncoding RNA as Modular Scaffold of Histone Modification Complexes
Tsai, M.-C., Manor, O., Wan, Y., Mosammaparast, N., Wang, J. K., Lan, F., Shi, Y., Segal, E., & Chang, H. Y. (2010) · Science · doi:10.1126/science.1192002 · Cited by 3,290 · cited 1×
“Protein interactions with long noncoding RNAs also play a role in epigenetic state changes during differentiation 7 , perhaps through ‘scaffolding’ chromatin remodel-ers 8,9 .”
BackgroundSelf-citationInsights into RNA Biology from an Atlas of Mammalian mRNA-Binding Proteins
Castello, A., Fischer, B., Eichelbaum, K., Horos, R., Beckmann, B. M., Strein, C., Davey, N. E., Humphreys, D. T., Preiss, T., Steinmetz, L. M., et al. (2012) · Cell · doi:10.1016/j.cell.2012.04.031 · Cited by 2,132
A compendium of RNA-binding motifs for decoding gene regulation
Ray, D., Kazan, H., Cook, K. B., Weirauch, M. T., Najafabadi, H. S., Li, X., Gueroussov, S., Albu, M., Zheng, H., Yang, A., et al. (2013) · Nature · doi:10.1038/nature12311 · Cited by 1,480 · cited 1×
“…dependence of RNA-protein interactions include medium-throughput microfluidic methods 18 and high-throughput methods coupling affinity-based selection with high-throughput DNA sequencing or array hybridization 19 , which recently have been used to generate a catalog of RNA binding motifs 20 .”
MethodHITS-CLIP yields genome-wide insights into brain alternative RNA processing
Licatalosi, D. D., Mele, A., Fak, J. J., Ule, J., Kayikci, M., Chi, S. W., Clark, T. A., Schweitzer, A. C., Blume, J. E., Wang, X., et al. (2008) · Nature · doi:10.1038/nature07488 · Cited by 1,352
RNA regulons: coordination of post-transcriptional events
Keene, J. D. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2111 · Cited by 1,321
Counting absolute numbers of molecules using unique molecular identifiers
Kivioja, T., Vähärautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., & Taipale, J. (2012) · Nature Methods · doi:10.1038/nmeth.1778 · Cited by 1,010 · cited 1×
“The high concentration of unlabeled MS2 protein blocks other binding sites on the array, preventing rebinding of fluorescently labeled MS2.”
BackgroundIn vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features
Ding, Y., Tang, Y., Kwok, C. K., Zhang, Y., Bevilacqua, P. C., & Assmann, S. M. (2014) · Nature · doi:10.1038/nature12756 · Cited by 775
Araya, C. L., Fowler, D. M., Chen, W., Muniez, I., Kelly, J. W., & Fields, S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209751109 · Cited by 230
Self-citation
Cited by
Long noncoding RNAs in cancer: mechanisms of action and technological advancements
Bartonicek, N., Maag, J. L. V., & Dinger, M. E. (2016) · Molecular Cancer · doi:10.1186/s12943-016-0530-6 · Cited by 459
RNA–protein interaction detection in living cells
Ramanathan, M., Majzoub, K., Rao, D. S., Neela, P. H., Zarnegar, B. J., Mondal, S., Roth, J. G., Gai, H., Kovalski, J. R., Siprashvili, Z., et al. (2018) · Nature Methods · doi:10.1038/nmeth.4601 · Cited by 308
Noncoding RNAs in neurodegeneration
Salta, E., & De Strooper, B. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.90 · Cited by 183
High-Throughput Analysis Reveals Rules for Target RNA Binding and Cleavage by AGO2
Becker, W. R., Ober-Reynolds, B., Jouravleva, K., Jolly, S. M., Zamore, P. D., & Greenleaf, W. J. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.06.012 · Cited by 161
Self-citationGómez-Marín, C., Tena, J. J., Acemel, R. D., López-Mayorga, M., Naranjo, S., de la Calle-Mustienes, E., Maeso, I., Beccari, L., Aneas, I., Vielmas, E., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1505463112 · Cited by 159
Metadata
- Authors
- Buenrostro, J. D., Araya, C. L., Chircus, L. M., Layton, C. J., Chang, H. Y., Snyder, M. P., & Greenleaf, W. J.
- Venue
- Nature Biotechnology
- Published
- 2014-04-13
- Volume
- 32
- Issue
- 6
- Pages
- 562-568
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2009301312
- Cited by
- 219
- References
- 11
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- green
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- Buenrostro, J. D., Araya, C. L., Chircus, L. M., Layton, C. J., Chang, H. Y., Snyder, M. P., & Greenleaf, W. J. (2014). Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes. Nature Biotechnology. https://doi.org/10.1038/nbt.2880
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