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The power of multiplexed functional analysis of genetic variants

Nature Protocols volume 11, pages 1782–1787 (2016)Cite this article

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Abstract

New technologies have recently enabled saturation mutagenesis and functional analysis of nearly all possible variants of regulatory elements or proteins of interest in single experiments. Here we discuss the past, present, and future of such multiplexed (functional) assays for variant effects (MAVEs). MAVEs provide detailed insight into sequence–function relationships, and they may prove critical for the prospective clinical interpretation of genetic variants.

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Figure 1: Multiplexed assays for variant effects (MAVEs) throughout the central dogma.
Figure 2: The key steps of MAVE.

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References

  1. Botstein, D. & Shortle, D. Strategies and applications of in vitro mutagenesis. Science 229, 1193–1201 (1985).

    Article  CAS  PubMed  Google Scholar 

  2. Fowler, D.M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801–807 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Inoue, F. & Ahituv, N. Decoding enhancers using massively parallel reporter assays. Genomics 106, 159–164 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Cunningham, B. & Wells, J. High-resolution epitope mapping of hGH-receptor interactions by alanine-scanning mutagenesis. Science 244, 1081–1085 (1989).

    Article  CAS  PubMed  Google Scholar 

  5. Smith, G. Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. Science 228, 1315–1317 (1985).

    Article  CAS  PubMed  Google Scholar 

  6. Boder, E.T. & Wittrup, K.D. Yeast surface display for screening combinatorial polypeptide libraries. Nat. Biotechnol. 15, 553–557 (1997).

    Article  CAS  PubMed  Google Scholar 

  7. Amstutz, P. et al. In vitro selection for catalytic activity with ribosome display. J. Am. Chem. Soc. 124, 9396–9403 (2002).

    Article  CAS  PubMed  Google Scholar 

  8. Myers, R., Tilly, K. & Maniatis, T. Fine structure genetic analysis of a beta-globin promoter. Science 232, 613–618 (1986).

    Article  CAS  PubMed  Google Scholar 

  9. Cleary, M.A. et al. Production of complex nucleic acid libraries using highly parallel in situ oligonucleotide synthesis. Nat. Methods 1, 241–248 (2004).

    Article  CAS  PubMed  Google Scholar 

  10. Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Ran, F.A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Findlay, G.M., Boyle, E.A., Hause, R.J., Klein, J.C. & Shendure, J. Saturation editing of genomic regions by multiplex homology-directed repair. Nature 513, 120–123 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. White, M.A. Understanding how cis-regulatory function is encoded in DNA sequence using massively parallel reporter assays and designed sequences. Genomics 106, 165–170 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Canver, M.C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wong, T.S., Roccatano, D., Zacharias, M. & Schwaneberg, U. A statistical analysis of random mutagenesis methods used for directed protein evolution. J. Mol. Biol. 355, 858–871 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Firnberg, E. & Ostermeier, M. PFunkel: efficient, expansive, user-defined mutagenesis. PLoS One 7, e52031 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jain, P.C. & Varadarajan, R. A rapid, efficient, and economical inverse polymerase chain reaction-based method for generating a site saturation mutant library. Anal. Biochem. 449, 90–98 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. McLaughlin, R.N. Jr., Poelwijk, F.J., Raman, A., Gosal, W.S. & Ranganathan, R. The spatial architecture of protein function and adaptation. Nature 491, 138–142 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kitzman, J.O., Starita, L.M., Lo, R.S., Fields, S. & Shendure, J. Massively parallel single-amino-acid mutagenesis. Nat. Methods 12, 203–206 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Melnikov, A., Rogov, P., Wang, L., Gnirke, A. & Mikkelsen, T.S. Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes. Nucleic Acids Res. 42, e112 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Naldini, L. et al. In vivo gene delivery and stable transduction of nondividing cells by a lentiviral vector. Science 272, 263–267 (1996).

    Article  CAS  PubMed  Google Scholar 

  24. Deyle, D.R. & Russell, D.W. Adeno-associated virus vector integration. Curr. Opin. Mol. Ther. 11, 442–447 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Craig, N.L. The mechanism of conservative site-specific recombination. Annu. Rev. Genet. 22, 77–105 (1988).

    Article  CAS  PubMed  Google Scholar 

  26. Sauer, B. Site-specific recombination: developments and applications. Curr. Opin. Biotechnol. 5, 521–527 (1994).

    Article  CAS  PubMed  Google Scholar 

  27. Patwardhan, R.P. et al. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis. Nat. Biotechnol. 27, 1173–1175 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Patwardhan, R.P. et al. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30, 265–270 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ke, S. et al. Quantitative evaluation of all hexamers as exonic splicing elements. Genome Res. 21, 1360–1374 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Rosenberg, A.B., Patwardhan, R.P., Shendure, J. & Seelig, G. Learning the sequence determinants of alternative splicing from millions of random sequences. Cell 163, 698–711 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Kinney, J.B., Murugan, A., Callan, C.G. Jr. & Cox, E.C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl. Acad. Sci. USA 107, 9158–9163 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fowler, D.M. et al. High-resolution mapping of protein sequence–function relationships. Nat. Methods 7, 741–746 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Starita, L.M. et al. Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis. Proc. Natl. Acad. Sci. USA 110, E1263–E1272 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Whitehead, T.A. et al. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat. Biotechnol. 30, 543–548 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Stiffler, M.A., Hekstra, D.R. & Ranganathan, R. Evolvability as a function of purifying selection in TEM-1 b-lactamase. Cell 160, 882–892 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Deng, Z. et al. Deep sequencing of systematic combinatorial libraries reveals b-lactamase sequence constraints at high resolution. J. Mol. Biol. 424, 150–167 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Melamed, D., Young, D.L., Miller, C.R. & Fields, S. Combining natural sequence variation with high throughput mutational data to reveal protein interaction sites. PLoS Genet. 11, e1004918 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Kim, I., Miller, C.R., Young, D.L. & Fields, S. High-throughput analysis of in vivo protein stability. Mol. Cell. Proteomics 12, 3370–3378 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hietpas, R.T., Jensen, J.D. & Bolon, D.N. Experimental illumination of a fitness landscape. Proc. Natl. Acad. Sci. USA 108, 7896–7901 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Thyagarajan, B. & Bloom, J.D. The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin. eLife 3, 1–26 (2014).

    Article  CAS  Google Scholar 

  42. Doud, M.B., Ashenberg, O. & Bloom, J.D. Site-specific amino acid preferences are mostly conserved in two closely related protein homologs. Mol. Biol. Evol. 32, 2944–2960 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bloom, J.D. An experimentally determined evolutionary model dramatically improves phylogenetic fit. Mol. Biol. Evol. 31, 1956–1978 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Doolan, K.M. & Colby, D.W. Conformation-dependent epitopes recognized by prion protein antibodies probed using mutational scanning and deep sequencing. J. Mol. Biol. 427, 328–340 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Starita, L.M. et al. Massively parallel functional analysis of BRCA1 RING domain variants. Genetics 200, 413–422 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Green, R.C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hiatt, J.B., Patwardhan, R.P., Turner, E.H., Lee, C. & Shendure, J. Parallel, tag-directed assembly of locally derived short sequence reads. Nat. Methods 7, 119–122 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bloom, J.D. Software for the analysis and visualization of deep mutational scanning data. BMC Bioinformatics 16, 168 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Fowler, D.M., Araya, C.L., Gerard, W. & Fields, S. Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics 27, 3430–3431 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Matuszewski, S., Hildebrandt, M.E., Ghenu, A.-H., Jensen, J.D. & Bank, C. A statistical guide to the design of deep mutational scanning experiments. Preprint at http://biorxiv.org/content/early/2016/06/29/048892 (2016).

  51. Ireland, W.T. & Kinney, J.B. Sort-Seq Tools: sequence-function relationship modeling for massively parallel assays. Preprint at http://biorxiv.org/content/early/2016/05/21/054676 (2016).

  52. White, M.A., Myers, C.A., Corbo, J.C. & Cohen, B.A. Massively parallel in vivo enhancer assay reveals that highly local features determine the cis-regulatory function of ChIP-seq peaks. Proc. Natl. Acad. Sci. USA 110, 11952–11957 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Majithia, A.R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc. Natl. Acad. Sci. USA 111, 13127–13132 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Exome Aggregation Consortium. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  55. Landrum, M.J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  57. Ardlie, K.G. et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

  58. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Xiong, H.Y. et al. The human splicing code reveals new insights into the genetic determinants of disease. Science 347, 1254806 (2015).

    Article  PubMed  CAS  Google Scholar 

  60. Maxwell, K.N. et al. Evaluation of ACMG-guideline-based variant classification of cancer susceptibility and non-cancer-associated genes in families affected by breast cancer. Am. J. Hum. Genet. 98, 801–817 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Vockley, C.M. et al. Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort. Genome Res. 25, 1206–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Tewhey, R. et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell 165, 1519–1529 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ulirsch, J.C. et al. Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell 165, 1530–1545 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Rich, M.S. et al. Comprehensive analysis of the SUL1 promoter of Saccharomyces cerevisiae. Genetics 203, 191–202 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kwasnieski, J.C., Mogno, I., Myers, C.A., Corbo, J.C. & Cohen, B.A. Complex effects of nucleotide variants in a mammalian cis-regulatory element. Proc. Natl. Acad. Sci. USA 109, 19498–19503 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Birnbaum, R.Y. et al. Systematic dissection of coding exons at single nucleotide resolution supports an additional role in cell-specific transcriptional regulation. PLoS Genet. 10, e1004592 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Holmqvist, E., Reimegård, J. & Wagner, E.G.H. Massive functional mapping of a 5-UTR by saturation mutagenesis, phenotypic sorting and deep sequencing. Nucleic Acids Res. 41, e122 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Dvir, S. et al. Deciphering the rules by which 5-UTR sequences affect protein expression in yeast. Proc. Natl. Acad. Sci. USA 110, E2792–E2801 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Shalem, O. et al. Systematic dissection of the sequence determinants of gene 3 end mediated expression control. PLoS Genet. 11, e1005147 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Procko, E. et al. Computational design of a protein-based enzyme inhibitor. J. Mol. Biol. 425, 3563–3575 (2013).

    Article  CAS  PubMed  Google Scholar 

  72. Lind, P.A., Berg, O.G. & Andersson, D.I. Mutational robustness of ribosomal protein genes. Science 330, 825–827 (2010).

    Article  CAS  PubMed  Google Scholar 

  73. Podgornaia, A.I. & Laub, M.T. Pervasive degeneracy and epistasis in a protein-protein interface. Science 347, 673–677 (2015).

    Article  CAS  PubMed  Google Scholar 

  74. Pál, G., Kouadio, J.L.K., Artis, D.R., Kossiakoff, A.A. & Sidhu, S.S. Comprehensive and quantitative mapping of energy landscapes for protein-protein interactions by rapid combinatorial scanning. J. Biol. Chem. 281, 22378–22385 (2006).

    Article  PubMed  CAS  Google Scholar 

  75. Sarkisyan, K.S. et al. Local fitness landscape of the green fluorescent protein. Nature 533, 397–401 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Adkar, B.V. et al. Protein model discrimination using mutational sensitivity derived from deep sequencing. Structure 20, 371–381 (2012).

    Article  CAS  PubMed  Google Scholar 

  77. Roscoe, B.P., Thayer, K.M., Zeldovich, K.B., Fushman, D. & Bolon, D.N.A. Analyses of the effects of all ubiquitin point mutants on yeast growth rate. 425, 1363–1377 (2013).

  78. Forsyth, C.M. et al. Deep mutational scanning of an antibody against epidermal growth factor receptor using mammalian cell display and massively parallel pyrosequencing. MAbs 5, 523–532 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Van Blarcom, T. et al. Precise and efficient antibody epitope determination through library design, yeast display and next-generation sequencing. J. Mol. Biol. 427, 1513–1534 (2015).

    Article  CAS  PubMed  Google Scholar 

  80. Shin, H. et al. Exploring the functional residues in a flavin-binding fluorescent protein using deep mutational scanning. PLoS One 9, e97817 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Gajula, K.S. et al. High-throughput mutagenesis reveals functional determinants for DNA targeting by activation-induced deaminase. Nucleic Acids Res. 42, 9964–9975 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank the Shendure lab, and in particular R. Hause, for discussions. M.G. is a National Science Foundation Graduate Research Fellow. J.S. is an Investigator of the Howard Hughes Medical Institute.

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Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA

    Molly Gasperini, Lea Starita & Jay Shendure

  2. Howard Hughes Medical Institute, Seattle, Washington, USA

    Jay Shendure

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M.G., L.S., and J.S. prepared the manuscript.

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Correspondence to Jay Shendure.

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Gasperini, M., Starita, L. & Shendure, J. The power of multiplexed functional analysis of genetic variants. Nat Protoc 11, 1782–1787 (2016). https://doi.org/10.1038/nprot.2016.135

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