Are the current gRNA ranking prediction algorithms useful for genome editing in plants?


Autoři: Fatima Naim aff001;  Kylie Shand aff001;  Satomi Hayashi aff001;  Martin O’Brien aff003;  James McGree aff004;  Alexander A. T. Johnson aff003;  Benjamin Dugdale aff001;  Peter M. Waterhouse aff001
Působiště autorů: Centre for Tropical Crops and Biocommodities, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia aff001;  Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Bentley, Western Australia, Australia aff002;  School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia aff003;  School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227994

Souhrn

Introducing a new trait into a crop through conventional breeding commonly takes decades, but recently developed genome sequence modification technology has the potential to accelerate this process. One of these new breeding technologies relies on an RNA-directed DNA nuclease (CRISPR/Cas9) to cut the genomic DNA, in vivo, to facilitate the deletion or insertion of sequences. This sequence specific targeting is determined by guide RNAs (gRNAs). However, choosing an optimum gRNA sequence has its challenges. Almost all current gRNA design tools for use in plants are based on data from experiments in animals, although many allow the use of plant genomes to identify potential off-target sites. Here, we examine the predictive uniformity and performance of eight different online gRNA-site tools. Unfortunately, there was little consensus among the rankings by the different algorithms, nor a statistically significant correlation between rankings and in vivo effectiveness. This suggests that important factors affecting gRNA performance and/or target site accessibility, in plants, are yet to be elucidated and incorporated into gRNA-site prediction tools.

Klíčová slova:

CRISPR – Guide RNA – Leaves – Nucleases – Plant genomes – Polymerase chain reaction – Sequence assembly tools – Sequence motif analysis


Zdroje

1. Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, et al. RNA-Guided Human Genome Engineering via Cas9. Science (New York, NY). 2013;339(6121):823–6.

2. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337(6096):816–21. doi: 10.1126/science.1225829 22745249

3. Yang H, Wang H, Shivalila Chikdu S, Cheng Albert W, Shi L, Jaenisch R. One-Step Generation of Mice Carrying Reporter and Conditional Alleles by CRISPR/Cas-Mediated Genome Engineering. Cell. 2013;154(6):1370–9. doi: 10.1016/j.cell.2013.08.022 23992847

4. Belhaj K, Chaparro-Garcia A, Kamoun S, Nekrasov V. Plant genome editing made easy: targeted mutagenesis in model and crop plants using the CRISPR/Cas system. Plant Methods. 2013;9(1):39. doi: 10.1186/1746-4811-9-39 24112467

5. Coutu C, Brandle J, Brown D, Brown K, Miki B, Simmonds J, et al. pORE: a modular binary vector series suited for both monocot and dicot plant transformation. Transgenic research. 2007;16(6):771–81. doi: 10.1007/s11248-007-9066-2 17273915

6. Xie K, Minkenberg B, Yang Y. Boosting CRISPR/Cas9 multiplex editing capability with the endogenous tRNA-processing system. Proceedings of the National Academy of Sciences. 2015;112(11):3570–5.

7. Lindbo JA. High-efficiency protein expression in plants from agroinfection-compatible Tobacco mosaic virus expression vectors. BMC Biotechnology. 2007;7:52-. doi: 10.1186/1472-6750-7-52 17723150

8. Naim F, Nakasugi K, Crowhurst RN, Hilario E, Zwart AB, Hellens RP, et al. Advanced engineering of lipid metabolism in Nicotiana benthamiana using a draft genome and the V2 viral silencing-suppressor protein. PLOS ONE. 2012;7(12):e52717. doi: 10.1371/journal.pone.0052717 23300750

9. Clough SJ, Bent AF. Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. The Plant journal: for cell and molecular biology. 1998;16(6):735–43.

10. Naim F, Shrestha P, Singh SP, Waterhouse PM, Wood CC. Stable expression of silencing-suppressor protein enhances the performance and longevity of an engineered metabolic pathway. Plant biotechnology journal. 2016;14(6):1418–26. doi: 10.1111/pbi.12506 26628000

11. Naim F, Dugdale B, Kleidon J, Brinin A, Shand K, Waterhouse P, et al. Gene editing the phytoene desaturase alleles of Cavendish banana using CRISPR/Cas9. Transgenic research. 2018.

12. Sallaud C, Meynard D, van Boxtel J, Gay C, Bes M, Brizard JP, et al. Highly efficient production and characterization of T-DNA plants for rice (Oryza sativa L.) functional genomics. TAG Theoretical and applied genetics Theoretische und angewandte Genetik. 2003;106(8):1396–408.

13. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28(12):1647–9. doi: 10.1093/bioinformatics/bts199 22543367

14. Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature biotechnology. 2016;34(2):184–91. doi: 10.1038/nbt.3437 26780180

15. Wong N, Liu W, Wang X. WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system. Genome Biology. 2015;16(1):218.

16. Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud J-B, et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biology. 2016;17(1):148. doi: 10.1186/s13059-016-1012-2 27380939

17. Stemmer M, Thumberger T, del Sol Keyer M, Wittbrodt J, Mateo JL. CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLOS ONE. 2015;10(4):e0124633. doi: 10.1371/journal.pone.0124633 25909470

18. Chari R, Yeo NC, Chavez A, Church GM. sgRNA Scorer 2.0: A Species-Independent Model To Predict CRISPR/Cas9 Activity. ACS Synthetic Biology. 2017;6(5):902–4. doi: 10.1021/acssynbio.6b00343 28146356

19. Liu H, Ding Y, Zhou Y, Jin W, Xie K, Chen L-L. CRISPR-P 2.0: An improved CRISPR-Cas9 tool for genome editing in plants. Molecular Plant. 2017;10(3):530–2. doi: 10.1016/j.molp.2017.01.003 28089950

20. Park J, Bae S, Kim J-S. Cas-Designer: a web-based tool for choice of CRISPR-Cas9 target sites. Bioinformatics. 2015;31(24):4014–6. doi: 10.1093/bioinformatics/btv537 26358729

21. Moreno-Mateos MA, Vejnar CE, Beaudoin J-D, Fernandez JP, Mis EK, Khokha MK, et al. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature Methods. 2015;12:982. doi: 10.1038/nmeth.3543 26322839

22. Philips JG, Naim F, Lorenc MT, Dudley KJ, Hellens RP, Waterhouse PM. The widely used Nicotiana benthamiana 16c line has an unusual T-DNA integration pattern including a transposon sequence. PLOS ONE. 2017;12(2):e0171311. doi: 10.1371/journal.pone.0171311 28231340

23. Ruiz MT, Voinnet O, Baulcombe DC. Initiation and maintenance of virus-induced gene silencing. Plant Cell. 1998;10(6):937–46. doi: 10.1105/tpc.10.6.937 9634582

24. Fusaro AF, Correa RL, Nakasugi K, Jackson C, Kawchuk L, Vaslin MFS, et al. The Enamovirus P0 protein is a silencing suppressor which inhibits local and systemic RNA silencing through AGO1 degradation. Virology. 2012;426(2):178–87. doi: 10.1016/j.virol.2012.01.026 22361475

25. Brazelton VA Jr, Zarecor S, Wright DA, Wang Y, Liu J, Chen K, et al. A quick guide to CRISPR sgRNA design tools. GM crops & food. 2016;6(4):266–76.

26. Wu X, Scott DA, Kriz AJ, Chiu AC, Hsu PD, Dadon DB, et al. Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Nature biotechnology. 2014;32(7):670–6. doi: 10.1038/nbt.2889 24752079

27. Xu H, Xiao T, Chen C-H, Li W, Meyer C, Wu Q, et al. Sequence determinants of improved CRISPR sgRNA design. Genome Research. 2015.

28. Wang T, Wei JJ, Sabatini DM, Lander ES. Genetic screens in human cells using the CRISPR-Cas9 system. Science. 2014;343(6166):80–4. doi: 10.1126/science.1246981 24336569

29. Bajic M, Maher KA, Deal RB. Identification of open chromatin regions in plant genomes using ATAC-Seq. Methods in molecular biology (Clifton, NJ). 2018;1675:183–201.

30. Maher KA, Bajic M, Kajala K, Reynoso M, Pauluzzi G, West DA, et al. Profiling of Accessible Chromatin Regions across Multiple Plant Species and Cell Types Reveals Common Gene Regulatory Principles and New Control Modules. The Plant Cell. 2018;30(1):15–36. doi: 10.1105/tpc.17.00581 29229750


Článek vyšel v časopise

PLOS One


2020 Číslo 1