From qualitative to quantitative: the state of the art and challenges for plant synthetic biology

Chenfei Tian, Jianhua Li, Yong Wang

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Quant. Biol. ›› 2023, Vol. 11 ›› Issue (3) : 214-230. DOI: 10.15302/J-QB-022-0326
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From qualitative to quantitative: the state of the art and challenges for plant synthetic biology

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Abstract

Backgrounds: As an increasing number of synthetic switches and circuits have been created for plant systems and of synthetic products produced in plant chassis, plant synthetic biology is taking a strong foothold in agriculture and medicine. The ever-exploding data has also promoted the expansion of toolkits in this field. Genetic parts libraries and quantitative characterization approaches have been developed. However, plant synthetic biology is still in its infancy. The considerations for selecting biological parts to design and construct genetic circuits with predictable functions remain desired.

Results: In this article, we review the current biotechnological progresses in field of plant synthetic biology. Assembly standardization and quantitative approaches of genetic parts and genetic circuits are discussed. We also highlight the main challenges in the iterative cycles of design-build-test-learn for introducing novel traits into plants.

Conclusion: Plant synthetic biology promises to provide important solutions to many issues in agricultural production, human health care, and environmental sustainability. However, tremendous challenges exist in this field. For example, the quantitative characterization of genetic parts is limited; the orthogonality and the transfer functions of circuits are unpredictable; and also, the mathematical modeling-assisted circuits design still needs to improve predictability and reliability. These challenges are expected to be resolved in the near future as interests in this field are intensifying.

Author summary

The flourishing plant science promotes the exploding number of data and the expansion of toolkits. Plant synthetic biology is still in its early stages and requires more quantitative and predictable study. Despite the challenges, some pioneering examples have been successfully demonstrated in model plants.

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Keywords

plant synthetic biology / quantitative characterization / genetic parts / genetic circuits

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Chenfei Tian, Jianhua Li, Yong Wang. From qualitative to quantitative: the state of the art and challenges for plant synthetic biology. Quant. Biol., 2023, 11(3): 214‒230 https://doi.org/10.15302/J-QB-022-0326

References

[1]
Endy, D. (2005). Foundations for engineering biology. Nature, 438: 449–453
CrossRef Google scholar
[2]
Zhang, L., Chang, S. (2011). Synthetic biology: from the first synthetic cell to see its current situation and future development. Chin. Sci. Bull., 56: 229–237
CrossRef Google scholar
[3]
Fausther-Bovendo, H. (2021). Plant-made vaccines and therapeutics. Science, 373: 740–741
CrossRef Google scholar
[4]
Zhu, X., Liu, X., Liu, T., Wang, Y., Ahmed, N., Li, Z. (2021). Synthetic biology of plant natural products: from pathway elucidation to engineered biosynthesis in plant cells. Plant Commun., 2: 100229
CrossRef Google scholar
[5]
Jiao, Y., Han, Y., Yang, Q., Huang, Y., An, J., Yang, Y. (2021). Commercialization development trend of genetically modified maize and the enlightenment. Shengwu Jishu Tongbao (in Chinese), 37: 164–176
[6]
Imamura, T., Isozumi, N., Higashimura, Y., Ohki, S. (2021). Production of ORF8 protein from SARS-CoV-2 using an inducible virus-mediated expression system in suspension-cultured tobacco BY-2 cells. Plant Cell Rep., 40: 433–436
CrossRef Google scholar
[7]
Diego-Martin, B., lez, B., Vazquez-Vilar, M., Selma, S., ndez, R., Gianoglio, S., ndez-Del-Carmen, A. (2020). Pilot production of SARS-CoV-2 related proteins in plants: a proof of concept for rapid repurposing of indoor farms into biomanufacturing facilities. Front. Plant Sci., 11: 612781
CrossRef Google scholar
[8]
Breitel, D., Brett, P., Alseekh, S., Fernie, A. R., Butelli, E. (2021). Metabolic engineering of tomato fruit enriched in L-DOPA. Metab. Eng., 65: 185–196
CrossRef Google scholar
[9]
Molina-Hidalgo, F. J., Vazquez-Vilar, M., Andrea, L., Demurtas, O. C., Fraser, P., Giuliano, G., Bock, R., ez, D. (2021). Engineering metabolism in nicotiana species: a promising future. Trends Biotechnol., 39: 901–913
CrossRef Google scholar
[10]
Akama, K., Kanetou, J., Shimosaki, S., Kawakami, K., Tsuchikura, S. (2009). Seed-specific expression of truncated OsGAD2 produces GABA-enriched rice grains that influence a decrease in blood pressure in spontaneously hypertensive rats. Transgenic Res., 18: 865–876
CrossRef Google scholar
[11]
Zhu, Q., Wang, B., Tan, J., Liu, T., Li, L. Liu, Y. (2019). Plant synthetic metabolic engineering for enhancing crop nutritional quality. Plant Commun., 1: 100017
CrossRef Google scholar
[12]
ller, K., Siegel, D., Rodriguez Jahnke, F., Gerrer, K., Wend, S., Decker, E. L., Reski, R., Weber, W. Zurbriggen, M. (2014). A red light-controlled synthetic gene expression switch for plant systems. Mol. Biosyst., 10: 1679–1688
CrossRef Google scholar
[13]
Chatelle, C., Ochoa-Fernandez, R., Engesser, R., Schneider, N., Beyer, H. M., Jones, A. R., Timmer, J., Zurbriggen, M. D. (2018). A green-light-responsive system for the control of transgene expression in mammalian and plant cells. ACS Synth. Biol., 7: 1349–1358
CrossRef Google scholar
[14]
Khakhar, A., Leydon, A. R., Lemmex, A. C., Klavins, E. Nemhauser, J. (2018). Synthetic hormone-responsive transcription factors can monitor and re-program plant development. eLife, 7: e34702
CrossRef Google scholar
[15]
Gomide, M. S., Sales, T. T., Barros, L. R. C., Limia, C. G., de Oliveira, M. A., Florentino, L. H., Barros, L. M. G., Robledo, M. L., Almeida, M. S. M. . (2020). Genetic switches designed for eukaryotic cells and controlled by serine integrases. Commun. Biol., 3: 255
CrossRef Google scholar
[16]
-Orts, J. M., Quijano-Rubio, A., Vazquez-Vilar, M., o-Bonillo, J., Moles-Casas, V., Selma, S., Gianoglio, S., Granell, A. (2020). A memory switch for plant synthetic biology based on the phage ϕC31 integration system. Nucleic Acids Res., 48: 3379–3394
CrossRef Google scholar
[17]
Lloyd, J. P. B., Ly, F., Gong, P., Pflueger, J., Swain, T., Pflueger, C., Fourie, E., Khan, M. A., Kidd, B. N. (2022). Synthetic memory circuits for stable cell reprogramming in plants. Nat. Biotechnol., 40: 1862–1872
CrossRef Google scholar
[18]
Brophy, J. A. N., Magallon, K. J., Duan, L., Zhong, V., Ramachandran, P., Kniazev, K. Dinneny, J. (2022). Synthetic genetic circuits as a means of reprogramming plant roots. Science, 377: 747–751
CrossRef Google scholar
[19]
Liu, J., Li, C. Q., Dong, Y., Yang, X. Wang, Y. (2018). Dosage imbalance of B- and C-class genes causes petaloid-stamen relating to F1 hybrid variation. BMC Plant Biol., 18: 341
CrossRef Google scholar
[20]
Dickinson, A. J., Zhang, J., Luciano, M., Wachsman, G., Sandoval, E., Schnermann, M., Dinneny, J. R. Benfey, P. (2021). A plant lipocalin promotes retinal-mediated oscillatory lateral root initiation. Science, 373: 1532–1536
CrossRef Google scholar
[21]
He, S., Yang, L., Ye, S., Lin, Y., Li, X., Wang, Y., Chen, G., Liu, G., Zhao, M., Zhao, X. . (2022). MPOD: Applications of integrated multi-omics database for medicinal plants. Plant Biotechnol. J., 20: 797–799
CrossRef Google scholar
[22]
Dreos, R., Ambrosini, G., Groux, R., rier, R. (2017). The eukaryotic promoter database in its 30th year: focus on non-vertebrate organisms. Nucleic Acids Res., 45: D51–D55
CrossRef Google scholar
[23]
Grau, J. Franco-Zorrilla, J. (2022). TDTHub, a web server tool for the analysis of transcription factor binding sites in plants. Plant J., 111: 1203–1215
CrossRef Google scholar
[24]
Kusunoki, K. Yamamoto, Y. (2017). Plant promoter database (PPDB). Methods Mol. Biol., 1533: 299–314
CrossRef Google scholar
[25]
Lescot, M., hais, P., Thijs, G., Marchal, K., Moreau, Y., Van de Peer, Y., (2002). PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res., 30: 325–327
CrossRef Google scholar
[26]
Kolar, K., Knobloch, C., Stork, H., (2018). OptoBase: a web platform for molecular optogenetics. ACS Synth. Biol., 7: 1825–1828
CrossRef Google scholar
[27]
Matys, V., Kel-Margoulis, O. V., Fricke, E., Liebich, I., Land, S., Barre-Dirrie, A., Reuter, I., Chekmenev, D., Krull, M., Hornischer, K. . (2006). TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res., 34: D108–D110
CrossRef Google scholar
[28]
Moisseyev, G., Park, K., Cui, A., Freitas, D., Rajagopal, D., Konda, A. R., Martin-Olenski, M., Mcham, M., Liu, K., Du, Q. . (2020). RGPDB: database of root-associated genes and promoters in maize, soybean, and sorghum. Database (Oxford), 2020: baaa038
CrossRef Google scholar
[29]
de Medeiros Oliveira, M., Bonadio, I., Lie de Melo, A., Mendes Souza, G. Durham, A. (2021). TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes. Brief. Bioinform., 22: bbab198
CrossRef Google scholar
[30]
Shahmuradov, I. A., Gammerman, A. J., Hancock, J. M., Bramley, P. M. Solovyev, V. (2003). PlantProm: a database of plant promoter sequences. Nucleic Acids Res., 31: 114–117
CrossRef Google scholar
[31]
Solovyev, V. V., Shahmuradov, I. A. Salamov, A. (2010). Identification of promoter regions and regulatory sites. Methods Mol. Biol., 674: 57–83
CrossRef Google scholar
[32]
Shahmuradov, I. A. Solovyev, V. (2015). Nsite, NsiteH and NsiteM computer tools for studying transcription regulatory elements. Bioinformatics, 31: 3544–3545
CrossRef Google scholar
[33]
Yilmaz, A., Mejia-Guerra, M. K., Kurz, K., Liang, X., Welch, L. (2011). AGRIS: the Arabidopsis gene regulatory information server, an update. Nucleic Acids Res., 39: D1118–D1122
CrossRef Google scholar
[34]
Hehl, R., Norval, L., Romanov, A. (2016). Boosting AthaMap database content with data from protein binding microarrays. Plant Cell Physiol., 57: e4
CrossRef Google scholar
[35]
Shahmuradov, I. A., Solovyev, V. V. Gammerman, A. (2005). Plant promoter prediction with confidence estimation. Nucleic Acids Res., 33: 1069–1076
CrossRef Google scholar
[36]
Shahmuradov, I. A., Umarov, R. K. Solovyev, V. (2017). TSSPlant: a new tool for prediction of plant Pol II promoters. Nucleic Acids Res., 45: gkw1353
CrossRef Google scholar
[37]
Cai, Y. M., Kallam, K., Tidd, H., Gendarini, G., Salzman, A. Patron, N. (2020). Rational design of minimal synthetic promoters for plants. Nucleic Acids Res., 48: 11845–11856
CrossRef Google scholar
[38]
Duvick, J., Fu, A., Muppirala, U., Sabharwal, M., Wilkerson, M. D., Lawrence, C. J., Lushbough, C. (2008). PlantGDB: a resource for comparative plant genomics. Nucleic Acids Res., 36: D959–D965
CrossRef Google scholar
[39]
Liu, Y., Wang, Z., Wu, X., Zhu, J., Luo, H., Tian, D., Li, C., Luo, J., Zhao, W., Hao, H. . (2021). SorGSD: updating and expanding the sorghum genome science database with new contents and tools. Biotechnol. Biofuels, 14: 165
CrossRef Google scholar
[40]
Chen, F., Dong, W., Zhang, J., Guo, X., Chen, J., Wang, Z., Lin, Z., Tang, H. (2018). The sequenced angiosperm genomes and genome databases. Front. Plant Sci., 9: 418
CrossRef Google scholar
[41]
Yang, Y., Lee, J. H., Poindexter, M. R., Shao, Y., Liu, W., Lenaghan, S. C., Ahkami, A. H., Blumwald, E. Stewart, C. N. (2021). Rational design and testing of abiotic stress-inducible synthetic promoters from poplar cis-regulatory elements. Plant Biotechnol. J., 19: 1354–1369
CrossRef Google scholar
[42]
Pedro, D. L. F., Amorim, T. S., Varani, A., Guyot, R., Domingues, D. S. Paschoal, A. (2021). An atlas of plant transposable elements. F1000 Res., 10: 1194
CrossRef Google scholar
[43]
Clarke, L. J. Kitney, R. (2016). Synthetic biology in the UK—An outline of plans and progress. Synth. Syst. Biotechnol., 1: 243–257
CrossRef Google scholar
[44]
Chung, S. M., Frankman, E. L. (2005). A versatile vector system for multiple gene expression in plants. Trends Plant Sci., 10: 357–361
CrossRef Google scholar
[45]
Nakagawa, T., Kurose, T., Hino, T., Tanaka, K., Kawamukai, M., Niwa, Y., Toyooka, K., Matsuoka, K., Jinbo, T. (2007). Development of series of gateway binary vectors, pGWBs, for realizing efficient construction of fusion genes for plant transformation. J. Biosci. Bioeng., 104: 34–41
CrossRef Google scholar
[46]
Smolke, C. (2009). Building outside of the box: iGEM and the BioBricks foundation. Nat. Biotechnol., 27: 1099–1102
CrossRef Google scholar
[47]
KnightT.. (2003) Idempotent Vector Design for Standard Assembly of Biobricks. Cambridge: Mit Artificial Intelligence Laboratory; Mit Synthetic Biology Working Group
[48]
Smedley, M. A. Harwood, W. (2015). Gateway®-compatible plant transformation vectors. Methods Mol. Biol., 1223: 3–16
CrossRef Google scholar
[49]
Karimi, M., Depicker, A. (2007). Recombinational cloning with plant gateway vectors. Plant Physiol., 145: 1144–1154
CrossRef Google scholar
[50]
Karimi, M., Bleys, A., Vanderhaeghen, R. (2007). Building blocks for plant gene assembly. Plant Physiol., 145: 1183–1191
CrossRef Google scholar
[51]
s-Bueno, M. D. M., Morao, A. K., Cayrel, A., Platre, M. P., Barberon, M., Caillieux, E., Colot, V., Jaillais, Y., Roudier, F. (2016). A versatile Multisite Gateway-compatible promoter and transgenic line collection for cell type-specific functional genomics in Arabidopsis. Plant J., 85: 320–333
CrossRef Google scholar
[52]
Engler, C., Kandzia, R. (2008). A one pot, one step, precision cloning method with high throughput capability. PLoS One, 3: e3647
CrossRef Google scholar
[53]
Patron, N. J., Orzaez, D., Marillonnet, S., Warzecha, H., Matthewman, C., Youles, M., Raitskin, O., Leveau, A., Rogers, C. . (2015). Standards for plant synthetic biology: a common syntax for exchange of DNA parts. New Phytol., 208: 13–19
CrossRef Google scholar
[54]
Engler, C., Youles, M., Gruetzner, R., Ehnert, T. M., Werner, S., Jones, J. D., Patron, N. J. (2014). A golden gate modular cloning toolbox for plants. ACS Synth. Biol., 3: 839–843
CrossRef Google scholar
[55]
Sarrion-PerdigonesA.,FalconiE.ZandalinasS.rezP.,ndez-del-CarmenA.,GranellA.. (2011) GoldenBraid: an iterative cloning system for standardized assembly of reusable genetic modules. PLoS One. 6, e21622
[56]
Sarrion-Perdigones, A., Vazquez-Vilar, M., Castelijns, B., Forment, J., Ziarsolo, P., Blanca, J., Granell, A. (2013). GoldenBraid 2. 0: a comprehensive DNA assembly framework for plant synthetic biology. Plant Physiol., 162: 1618–1631
CrossRef Google scholar
[57]
Vazquez-Vilar, M., Quijano-Rubio, A., Fernandez-Del-Carmen, A., Sarrion-Perdigones, A., Ochoa-Fernandez, R., Ziarsolo, P., Blanca, J., Granell, A. (2017). GB3.0: a platform for plant bio-design that connects functional DNA elements with associated biological data. Nucleic Acids Res., 45: 2196–2209
CrossRef Google scholar
[58]
Vazquez-VilarM.,Garcia-CarpinteroV.,SelmaS.,. M., Sanchez-Vicente, J., Salazar-Sarasua, B., Ressa, A., de Paola, C., Ajenjo, M., Quintela, J. C., et al. (2021) The GB4.0 platform, an All-In-One tool for CRISPR/Cas-based multiplex genome engineering in plants. Front. Plant Sci., 12, 689937
[59]
Vazquez-Vilar, M., Juarez, P., -Orts, J. M. (2022). Design of multiplexing CRISPR/Cas9 constructs for plant genome engineering using the GoldenBraid DNA assembly standard. Methods Mol. Biol., 2379: 27–44
CrossRef Google scholar
[60]
lez, B., Vazquez-Vilar, M., nchez-Vicente, J. (2022). Optimization of vectors and targeting strategies including GoldenBraid and genome editing tools: GoldenBraid assembly of multiplex CRISPR/Cas12a guide RNAs for gene editing in Nicotiana benthamiana. Methods Mol. Biol., 2480: 193–214
CrossRef Google scholar
[61]
Vemanna, R. S., Chandrashekar, B. K., Hanumantha Rao, H. M., Sathyanarayanagupta, S. K., Sarangi, K. S., Nataraja, K. N. (2013). A modified MultiSite gateway cloning strategy for consolidation of genes in plants. Mol. Biotechnol., 53: 129–138
CrossRef Google scholar
[62]
Shih, P. M., Vuu, K., Mansoori, N., Ayad, L., Louie, K. B., Bowen, B. P., Northen, T. R. (2016). A robust gene-stacking method utilizing yeast assembly for plant synthetic biology. Nat. Commun., 7: 13215
CrossRef Google scholar
[63]
Zhu, Q., Zeng, D., Yu, S., Cui, C., Li, J., Li, H., Chen, J., Zhang, R., Zhao, X., Chen, L. . (2018). From golden rice to aSTARice: Bioengineering astaxanthin biosynthesis in rice endosperm. Mol. Plant, 11: 1440–1448
CrossRef Google scholar
[64]
Zhu, Q., Yu, S., Zeng, D., Liu, H., Wang, H., Yang, Z., Xie, X., Shen, R., Tan, J., Li, H. . (2017). Development of “purple endosperm rice” by engineering anthocyanin biosynthesis in the endosperm with a high-efficiency transgene stacking system. Mol. Plant, 10: 918–929
CrossRef Google scholar
[65]
Lin, L., Liu, Y. G., Xu, X. (2003). Efficient linking and transfer of multiple genes by a multigene assembly and transformation vector system. Proc. Natl. Acad. Sci. USA, 100: 5962–5967
CrossRef Google scholar
[66]
Zhao, Y., Han, J., Tan, J., Yang, Y., Li, S., Gou, Y., Luo, Y., Li, T., Xiao, W., Xue, Y. . (2022). Efficient assembly of long DNA fragments and multiple genes with improved nickase-based cloning and Cre/loxP recombination. Plant Biotechnol. J., 20: 1983–1995
CrossRef Google scholar
[67]
Altpeter, F., Springer, N. M., Bartley, L. E., Blechl, A. E., Brutnell, T. P., Citovsky, V., Conrad, L. J., Gelvin, S. B., Jackson, D. P., Kausch, A. P. . (2016). Advancing crop transformation in the era of genome editing. Plant Cell, 28: 1510–1520
CrossRef Google scholar
[68]
Schaumberg, K. A., Antunes, M. S., Kassaw, T. K., Xu, W., Zalewski, C. S., Medford, J. I. (2016). Quantitative characterization of genetic parts and circuits for plant synthetic biology. Nat. Methods, 13: 94–100
CrossRef Google scholar
[69]
Matsuo, N., Minami, M., Maeda, T. (2001). Dual luciferase assay for monitoring transient gene expression in higher plants. Plant Biotechnol. (Tsukuba), 18: 71–75
CrossRef Google scholar
[70]
Jores, T., Tonnies, J., Dorrity, M. W., Cuperus, J. T., Fields, S. (2020). Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves. Plant Cell, 32: 2120–2131
CrossRef Google scholar
[71]
Kim, Y. S., Johnson, G. D., Seo, J., Barrera, A., Cowart, T. N., Majoros, W. H., Ochoa, A., Allen, A. S. Reddy, T. (2021). Correcting signal biases and detecting regulatory elements in STARR-seq data. Genome Res., 31: 877–889
CrossRef Google scholar
[72]
Jores, T., Tonnies, J., Wrightsman, T., Buckler, E. S., Cuperus, J. T., Fields, S. (2021). Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters. Nat. Plants, 7: 842–855
CrossRef Google scholar
[73]
Sun, J., He, N., Niu, L., Huang, Y., Shen, W., Zhang, Y., Li, L. (2019). Global quantitative mapping of enhancers in rice by STARR-seq. Genom. Proteom. Bioinf., 17: 140–153
CrossRef Google scholar
[74]
TianC.,ZhangY.,LiJ.. (2022) Benchmarking intrinsic promoters and terminators for plant synthetic biology research. BioDesign Research., 2022
[75]
AndreouA. I.,NirkkoJ.,Ochoa-VillarrealM.. (2021) Mobius assembly for plant systems highlights promoter-terminator interaction in gene regulation. bioRxiv doi: 10.1101/2021.03.31.437819
[76]
Gunadi, A., Rushton, P. J., Mchale, L. K., Gutek, A. H. Finer, J. (2016). Characterization of 40 soybean (Glycine max) promoters, isolated from across 5 thematic gene groups. Plant Cell Tissue Organ Cult., 127: 1–16
CrossRef Google scholar
[77]
Kakei, Y., Masuda, H., Nishizawa, N. K., Hattori, H. Aung, M. (2021). Elucidation of novel cis-regulatory elements and promoter structures involved in iron excess response mechanisms in rice using a bioinformatics approach. Front. Plant Sci., 12: 660303
CrossRef Google scholar
[78]
Kaur, A., Pati, P. K., Pati, A. M. Nagpal, A. (2017). In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa. PLoS One, 12: e0184523
CrossRef Google scholar
[79]
Schmitz, R. J., Grotewold, E. (2022). Cis-regulatory sequences in plants: their importance, discovery, and future challenges. Plant Cell, 34: 718–741
CrossRef Google scholar
[80]
Basu, D. South, P. (2022). Design and analysis of native photorespiration gene motifs of promoter untranslated region combinations under short term abiotic stress conditions. Front. Plant Sci., 13: 828729
CrossRef Google scholar
[81]
To, J. P. C., Davis, I. W., Marengo, M. S., Shariff, A., Baublite, C., Decker, K., Gao, Z., Haragutchi, O., Jung, J. W. . (2021). Expression elements derived from plant sequences provide effective gene expression regulation and new opportunities for plant biotechnology traits. Front. Plant Sci., 12: 712179
CrossRef Google scholar
[82]
Davis, I. W., Benninger, C., Benfey, P. N. (2012). POWRS: position-sensitive motif discovery. PLoS One, 7: e40373
CrossRef Google scholar
[83]
McCarthy, D. M. Medford, J. (2020). Quantitative and predictive genetic parts for plant synthetic biology. Front. Plant Sci., 11: 512526
CrossRef Google scholar
[84]
Han, L., Silvestre, S., Sayanova, O., Haslam, R. P. Napier, J. (2022). Using field evaluation and systematic iteration to rationalise the accumulation of omega-3 long-chain polyunsaturated fatty acids in transgenic Camelina sativa. Plant Biotechnol. J., 20: 1833–1852
CrossRef Google scholar
[85]
Petrie, J. R., Zhou, X. R., Leonforte, A., McAllister, J., Shrestha, P., Kennedy, Y., Belide, S., Buzza, G., Gororo, N., Gao, W. . (2020). Development of a Brassica napus (Canola) crop containing fish oil-like levels of DHA in the seed oil. Front. Plant Sci., 11: 727
CrossRef Google scholar
[86]
Belide, S., Shrestha, P., Kennedy, Y., Leonforte, A., Devine, M. D., Petrie, J. R., Singh, S. P. Zhou, X. (2022). Engineering docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA) in Brassica juncea. Plant Biotechnol. J., 20: 19–21
CrossRef Google scholar
[87]
Yan-yan, L., Li-na, G., Cheng-zhen, L. Zhi-gang, M. Li, Y., Guo, L., Liang, C., Meng, Z., Tahira, S., Guo, S. (2022). Overexpression of Brassica napus cytosolic fructose-1,6-bisphosphatase and sedoheptulose-1,7-bisphosphatase genes significantly enhanced tobacco growth and biomass. J. Integr. Agric., 21: 49–59
[88]
Fan, H., Liu, Y., Li, C. Y., Jiang, Y., Song, J. J., Yang, L., Zhao, Q., Hu, Y. H., Chen, X. Y. Xu, J. (2021). Engineering high coenzyme Q10 tomato. Metab. Eng., 68: 86–93
CrossRef Google scholar
[89]
Forestier, E. C. F., Czechowski, T., Cording, A. C., Gilday, A. D., King, A. J., Brown, G. D. Graham, I. (2021). Developing a Nicotiana benthamiana transgenic platform for high-value diterpene production and candidate gene evaluation. Plant Biotechnol. J., 19: 1614–1623
CrossRef Google scholar
[90]
Davis, K., Gkotsi, D. S., Smith, D. R. M., Goss, R. J. M., Caputi, L. Connor, S. (2020). Nicotiana benthamiana as a transient expression host to produce auxin analogs. Front. Plant Sci., 11: 581675
CrossRef Google scholar
[91]
Allen, Q. M., Febres, V. J., Rathinasabapathi, B. Chaparro, J. (2022). Engineering a plant-derived astaxanthin synthetic pathway into Nicotiana benthamiana. Front. Plant Sci., 12: 831785
CrossRef Google scholar
[92]
Narayanan, N., Beyene, G., Chauhan, R. D., Gehan, J., Butts, P., Siritunga, D., Okwuonu, I., Woll, A., nez-Aguilar, D. M. . (2019). Biofortification of field-grown cassava by engineering expression of an iron transporter and ferritin. Nat. Biotechnol., 37: 144–151
CrossRef Google scholar
[93]
Liang, Q., Wang, K., Liu, X., Riaz, B., Jiang, L., Wan, X., Ye, X. (2019). Improved folate accumulation in genetically modified maize and wheat. J. Exp. Bot., 70: 1539–1551
CrossRef Google scholar
[94]
Liu, X., Ma, X., Wang, H., Li, S., Yang, W., Nugroho, R. D., Luo, L., Zhou, X., Tang, C., Fan, Y. . (2021). Metabolic engineering of astaxanthin-rich maize and its use in the production of biofortified eggs. Plant Biotechnol. J., 19: 1812–1823
CrossRef Google scholar
[95]
Nett, R. S., Lau, W. Sattely, E. (2020). Discovery and engineering of colchicine alkaloid biosynthesis. Nature, 584: 148–153
CrossRef Google scholar
[96]
Li, J., Mutanda, I., Wang, K., Yang, L., Wang, J. (2019). Chloroplastic metabolic engineering coupled with isoprenoid pool enhancement for committed taxanes biosynthesis in Nicotiana benthamiana. Nat. Commun., 10: 4850
CrossRef Google scholar
[97]
ndez, R., nez, E., Gianoglio, S., Quijano-Rubio, A., Rubert, A., Rambla, J. L., Vazquez-Vilar, M., Huet, E. . (2021). Production of volatile moth sex pheromones in transgenic Nicotiana benthamiana plants. BioDesign Research., 2021: 9891082
CrossRef Google scholar
[98]
Iacopino, S., Jurinovich, S., Cupellini, L., Piccinini, L., Cardarelli, F., Perata, P., Mennucci, B., Giuntoli, B. (2019). A synthetic oxygen sensor for plants based on animal hypoxia signaling. Plant Physiol., 179: 986–1000
CrossRef Google scholar
[99]
Nemhauser, J. L. Torii, K. (2016). Plant synthetic biology for molecular engineering of signalling and development. Nat. Plants, 2: 16010
CrossRef Google scholar
[100]
Swinnen, G., Goossens, A. (2016). Lessons from domestication: targeting cis-regulatory elements for crop improvement. Trends Plant Sci., 21: 506–515
CrossRef Google scholar
[101]
Andres, J., Blomeier, T. Zurbriggen, M. (2019). Synthetic switches and regulatory circuits in plants. Plant Physiol., 179: 862–884
CrossRef Google scholar
[102]
Zhou, Y., Ding, M., Gao, S., Yu-Strzelczyk, J., Krischke, M., Duan, X., Leide, J., Riederer, M., Mueller, M. J., Hedrich, R. . (2021). Optogenetic control of plant growth by a microbial rhodopsin. Nat. Plants, 7: 144–151
CrossRef Google scholar
[103]
Liu, L., Gallagher, J., Arevalo, E. D., Chen, R., Skopelitis, T., Wu, Q., Bartlett, M. (2021). Enhancing grain-yield-related traits by CRISPR-Cas9 promoter editing of maize CLE genes. Nat. Plants, 7: 287–294
CrossRef Google scholar
[104]
guez-Leal, D., Lemmon, Z. H., Man, J., Bartlett, M. E. Lippman, Z. (2017). Engineering quantitative trait variation for crop improvement by genome editing. Cell, 171: 470–480.e8
CrossRef Google scholar
[105]
Song, X., Meng, X., Guo, H., Cheng, Q., Jing, Y., Chen, M., Liu, G., Wang, B., Wang, Y., Li, J. . (2022). Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nat. Biotechnol., 40: 1403–1411
CrossRef Google scholar
[106]
Lowder, L. G., Zhou, J., Zhang, Y., Malzahn, A., Zhong, Z., Hsieh, T. F., Voytas, D. F., Zhang, Y. (2018). Robust transcriptional activation in plants using multiplexed CRISPR-Act2. 0 and mTALE-Act systems. Mol. Plant, 11: 245–256
CrossRef Google scholar
[107]
Lowder, L. G., Paul, J. W. (2017). Multiplexed transcriptional activation or repression in plants using CRISPR-dCas9-based systems. Methods Mol. Biol., 1629: 167–184
CrossRef Google scholar
[108]
Lowder, L. G., Zhang, D., Baltes, N. J., Paul, J. W. Tang, X., Zheng, X., Voytas, D. F., Hsieh, T. F., Zhang, Y. (2015). A CRISPR/Cas9 toolbox for multiplexed plant genome editing and transcriptional regulation. Plant Physiol., 169: 971–985
CrossRef Google scholar
[109]
Chavez, A., Scheiman, J., Vora, S., Pruitt, B. W., Tuttle, M., P R Iyer, E., Lin, S., Kiani, S., Guzman, C. D., Wiegand, D. J. . (2015). Highly efficient Cas9-mediated transcriptional programming. Nat. Methods, 12: 326–328
CrossRef Google scholar
[110]
Sajwan, S. (2019). Gene activation by dCas9-CBP and the SAM system differ in target preference. Sci. Rep., 9: 18104
CrossRef Google scholar
[111]
Zalatan, J. G., Lee, M. E., Almeida, R., Gilbert, L. A., Whitehead, E. H., La Russa, M., Tsai, J. C., Weissman, J. S., Dueber, J. E., Qi, L. S. . (2015). Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell, 160: 339–350
CrossRef Google scholar
[112]
Zhou, H., Liu, J., Zhou, C., Gao, N., Rao, Z., Li, H., Hu, X., Li, C., Yao, X., Shen, X. . (2018). In vivo simultaneous transcriptional activation of multiple genes in the brain using CRISPR-dCas9-activator transgenic mice. Nat. Neurosci., 21: 440–446
CrossRef Google scholar
[113]
Li, Z., Zhang, D., Xiong, X., Yan, B., Xie, W., Sheen, J. Li, J. (2017). A potent Cas9-derived gene activator for plant and mammalian cells. Nat. Plants, 3: 930–936
CrossRef Google scholar
[114]
Pan, C., Wu, X., Markel, K., Malzahn, A. A., Kundagrami, N., Sretenovic, S., Zhang, Y., Cheng, Y., Shih, P. M. (2021). CRISPR-Act3. 0 for highly efficient multiplexed gene activation in plants. Nat. Plants, 7: 942–953
CrossRef Google scholar
[115]
Selma, S., -Orts, J. M., Vazquez-Vilar, M., Diego-Martin, B., Ajenjo, M., Garcia-Carpintero, V., Granell, A. (2019). Strong gene activation in plants with genome-wide specificity using a new orthogonal CRISPR/Cas9-based programmable transcriptional activator. Plant Biotechnol. J., 17: 1703–1705
CrossRef Google scholar
[116]
Selma, S., Espinosa-Ruiz, A., Gianoglio, S., Lopez-Gresa, M. P., zquez-Vilar, M., Flors, V., Granell, A. (2022). Custom-made design of metabolite composition in N. benthamiana leaves using CRISPR activators. Plant Biotechnol. J., 20: 1578–1590
CrossRef Google scholar
[117]
Pan, C., Li, G., Malzahn, A. A., Cheng, Y., Leyson, B., Sretenovic, S., Gurel, F., Coleman, G. D. (2022). Boosting plant genome editing with a versatile CRISPR-Combo system. Nat. Plants, 8: 513–525
CrossRef Google scholar
[118]
Dey, N., Sarkar, S., Acharya, S. Maiti, I. (2015). Synthetic promoters in planta. Planta, 242: 1077–1094
CrossRef Google scholar
[119]
Aysha, J., Noman, M., Wang, F., Liu, W., Zhou, Y., Li, H. (2018). Synthetic promoters: designing the cis regulatory modules for controlled gene expression. Mol. Biotechnol., 60: 608–620
CrossRef Google scholar
[120]
PandiarajanR.. (2018) In vivo promoter engineering in plants: are we ready? Plant Sci., 277, 132–138
[121]
Ouma, W. Z., Pogacar, K. (2018). Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. PLOS Comput. Biol., 14: e1006098
CrossRef Google scholar
[122]
Core, L. J., Martins, A. L., Danko, C. G., Waters, C. T., Siepel, A. Lis, J. (2014). Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet., 46: 1311–1320
CrossRef Google scholar
[123]
Ricci, W. A., Lu, Z., Ji, L., Marand, A. P., Ethridge, C. L., Murphy, N. G., Noshay, J. M., Galli, M., a-Guerra, M. K., . (2019). Widespread long-range cis-regulatory elements in the maize genome. Nat. Plants, 5: 1237–1249
CrossRef Google scholar
[124]
Fagny, M., Kuijjer, M. L., Stam, M., Joets, J., Turc, O., re, J., Pateyron, S., Venon, A. (2021). Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks. Front. Genet., 11: 606285
CrossRef Google scholar
[125]
Ding, W., Cheng, J., Guo, D., Mao, L., Li, J., Lu, L., Zhang, Y., Yang, J. (2018). Engineering the 5′UTR-mediated regulation of protein abundance in yeast using nucleotide sequence activity relationships. ACS Synth. Biol., 7: 2709–2714
CrossRef Google scholar
[126]
Mutalik, V. K., Guimaraes, J. C., Cambray, G., Lam, C., Christoffersen, M. J., Mai, Q. A., Tran, A. B., Paull, M., Keasling, J. D., Arkin, A. P. . (2013). Precise and reliable gene expression via standard transcription and translation initiation elements. Nat. Methods, 10: 354–360
CrossRef Google scholar
[127]
Chen, Y., Zhang, S., Young, E. M., Jones, T. S., Densmore, D. Voigt, C. (2020). Genetic circuit design automation for yeast. Nat. Microbiol., 5: 1349–1360
CrossRef Google scholar
[128]
Xia, P. F., Ling, H., Foo, J. L. Chang, M. (2019). Synthetic genetic circuits for programmable biological functionalities. Biotechnol. Adv., 37: 107393
CrossRef Google scholar
[129]
Van Brempt, M., Clauwaert, J., Mey, F., Stock, M., Maertens, J., Waegeman, W. (2020). Predictive design of sigma factor-specific promoters. Nat. Commun., 11: 5822
CrossRef Google scholar
[130]
Nielsen, A. A., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D. Voigt, C. (2016). Genetic circuit design automation. Science, 352: aac7341
CrossRef Google scholar
[131]
Jones, T. S., Oliveira, S. M. D., Myers, C. J., Voigt, C. A. (2022). Genetic circuit design automation with Cello 2. 0. Nat. Protoc., 17: 1097–1113
CrossRef Google scholar
[132]
Kwok, R. (2010). Five hard truths for synthetic biology. Nature, 463: 288–290
CrossRef Google scholar
[133]
Shin, J., Zhang, S., Der, B. S., Nielsen, A. A. Voigt, C. (2020). Programming Escherichia coli to function as a digital display. Mol. Syst. Biol., 16: e9401
CrossRef Google scholar
[134]
Crowther, M., Wipat, A. (2022). A network approach to genetic circuit designs. ACS Synth. Biol., 11: 3058–3066
CrossRef Google scholar
[135]
lez, A. (2019). Benefits of using genomic insulators flanking transgenes to increase expression and avoid positional effects. Sci. Rep., 9: 8474
CrossRef Google scholar
[136]
Puchta, H., Jiang, J., Wang, K. (2022). Updates on gene editing and its applications. Plant Physiol., 188: 1725–1730
CrossRef Google scholar
[137]
Neill, B. M., Mikkelson, K. L., Gutierrez, N. M., Cunningham, J. L., Wolff, K. L., Szyjka, S. J., Yohn, C. B., Redding, K. E. Mendez, M. (2012). An exogenous chloroplast genome for complex sequence manipulation in algae. Nucleic Acids Res., 40: 2782–2792
CrossRef Google scholar
[138]
Shao, Y., Lu, N., Wu, Z., Cai, C., Wang, S., Zhang, L. L., Zhou, F., Xiao, S., Liu, L., Zeng, X. . (2018). Creating a functional single-chromosome yeast. Nature, 560: 331–335
CrossRef Google scholar
[139]
Tomita, M. (2001). Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol., 19: 205–210
CrossRef Google scholar
[140]
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B. Assad-Garcia, N., Glass, J. I. Covert, M. (2012). A whole-cell computational model predicts phenotype from genotype. Cell, 150: 389–401
CrossRef Google scholar
[141]
Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I. . (2020). Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science, 369: eaav3751
CrossRef Google scholar
[142]
Maritan, M., Autin, L., Karr, J., Covert, M. W., Olson, A. J. Goodsell, D. (2022). Building structural models of a whole mycoplasma cell. J. Mol. Biol., 434: 167351
CrossRef Google scholar
[143]
Lu, H., Kerkhoven, E. J. (2022). Multiscale models quantifying yeast physiology: towards a whole-cell model. Trends Biotechnol., 40: 291–305
CrossRef Google scholar
[144]
Beard, D. A., Neal, M. L., Tabesh-Saleki, N., Thompson, C. T., Bassingthwaighte, J. B., Shimoyama, M. Carlson, B. (2012). Multiscale modeling and data integration in the virtual physiological rat project. Ann. Biomed. Eng., 40: 2365–2378
CrossRef Google scholar
[145]
Marshall-Colon, A., Long, S. P., Allen, D. K., Allen, G., Beard, D. A., Benes, B., von Caemmerer, S., Christensen, A. J., Cox, D. J., Hart, J. C. . (2017). Crops in silico: generating virtual crops using an integrative and multi-scale modeling platform. Front. Plant Sci., 8: 786
CrossRef Google scholar
[146]
Zhu, X. G., Lynch, J. P., LeBauer, D. S., Millar, A. J., Stitt, M. Long, S. (2016). Plants in silico: why, why now and what?—an integrative platform for plant systems biology research. Plant Cell Environ., 39: 1049–1057
CrossRef Google scholar
[147]
Zheng, H., Ho, P. Y., Jiang, M., Tang, B., Liu, W., Li, D., Yu, X., Kleckner, N. E., Amir, A. (2016). Interrogating the Escherichia coli cell cycle by cell dimension perturbations. Proc. Natl. Acad. Sci. USA, 113: 15000–15005
CrossRef Google scholar
[148]
Du, P., Zhao, H., Zhang, H., Wang, R., Huang, J., Tian, Y., Luo, X., Luo, X., Wang, M., Xiang, Y. . (2020). De novo design of an intercellular signaling toolbox for multi-channel cell-cell communication and biological computation. Nat. Commun., 11: 4226
CrossRef Google scholar
[149]
Milias-Argeitis, A., Rullan, M., Aoki, S. K., Buchmann, P. (2016). Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat. Commun., 7: 12546
CrossRef Google scholar
[150]
Olson, E. J., Hartsough, L. A., Landry, B. P., Shroff, R. Tabor, J. (2014). Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nat. Methods, 11: 449–455
CrossRef Google scholar
[151]
Carignano, A., Chen, D. H., Mallory, C., Wright, R. C., Seelig, G. (2022). Modular, robust, and extendible multicellular circuit design in yeast. eLife, 11: e74540
CrossRef Google scholar
[152]
Dorrity, M. W., Alexandre, C. M., Hamm, M. O., Vigil, A. L., Fields, S., Queitsch, C. Cuperus, J. (2021). The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. Nat. Commun., 12: 3334
CrossRef Google scholar
[153]
Farmer, A., Thibivilliers, S., Ryu, K. H., Schiefelbein, J. (2021). Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level. Mol. Plant, 14: 372–383
CrossRef Google scholar
[154]
Okubo-Kurihara, E., Ali, A., Hiramoto, M., Kurihara, Y., Abouleila, Y., Abdelazem, E. M., Kawai, T., Makita, Y., Kawashima, M., Esaki, T. . (2022). Tracking metabolites at single-cell resolution reveals metabolic dynamics during plant mitosis. Plant Physiol., 189: 459–464
CrossRef Google scholar
[155]
Marand, A. P., Chen, Z., Gallavotti, A. Schmitz, R. (2021). A cis-regulatory atlas in maize at single-cell resolution. Cell, 184: 3041–3055.e21
CrossRef Google scholar

ACKNOWLEDGEMENTS

This work was supported by the National Key Research and Development Program of China (No. 2018YFA0900600), the Strategic Priority Research Program “Molecular mechanism of Plant Growth and Development” of Chinese Academy of Science (No. XDB27020202), the National Natural Science Foundation of China (Nos. 22077129, 32070328 and 41876084), the Natural Science Foundation of Shanghai Municipal Science and Technology Committee (No. 21ZR1470900), the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project (No. TSBICIP-KJGG-002-15), the Program of Shanghai Academic Research Leader (No. 20XD1404400). This work was also financially supported by the Construction of the Registry and Database of Bioparts for Synthetic Biology of the Chinese Academy of Science (No. ZSYS-016), the International Partnership Program of Chinese Academy of Science (No. 153D31KYSB20170121), and the National Key Laboratory of Plant Molecular Genetics, SIPPE, Chinese Academy of Science.

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Conflicts of interest The authors Chenfei Tian, Jianhua Li, and Yong Wang declare that they have no conflict of interests.
This article is a review and does not contain any human or animal subjects performed by any of the authors.

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