The design of synthetic gene circuits in plants: new components, old challenges

Vázquez‐Vilar, Marta; Selma, Sara; Orzáez, Diego

doi:10.1093/jxb/erad167

Cited by 11 publications

(8 citation statements)

References 146 publications

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“…However, many bottlenecks must be addressed before plant synthetic biology-derived products could be made available (Vazquez-Vilar et al, 2023) and, therefore, developing new tools to push the boundaries of plant synthetic biology is essential to unlock its full potential.…”

Section: Discussionmentioning

confidence: 99%

“…Plant synthetic biology can greatly contribute to the next Green Revolution, by engineering crops for a more sustainable agriculture (Kocaoglan et al, 2023; Wang & Demirer 2023; Yang & Reyna-Llorens 2023) and also for bioproduction (Burnett & Burnett 2020; Yang et al, 2022). In the past two decades, efforts in creating cutting-edge approaches in cloning, part standardization, plant transformation, genome engineering, biosensors and genetic circuits (Patron 2020; Vazquez-Vilar et al, 2023; Yang & Reyna-Llorens 2023; Kocaoglan et al, 2023) have profoundly impacted plant synthetic biology. However, many bottlenecks must be addressed before plant synthetic biology-derived products could be made available (Vazquez-Vilar et al, 2023) and, therefore, developing new tools to push the boundaries of plant synthetic biology is essential to unlock its full potential.…”

Section: Discussionmentioning

confidence: 99%

“…Genetically-encoded biosensors have been used for several applications in plants. Much attention has been given to biosensors capable of detecting and reporting in planta the spatio-temporal levels of specific molecules, such as sugars and hormones (reviewed by Walia et al, 2018; Wright & Nemhauser 2019; Vazquez-Vilar et al, 2023). Other studies have used biosensors to engineer expression systems inducible by exogenous substances or to detect molecules in the environment (Dugdale et al, 2013; Garcia-Perez et al, 2022), working as basic on-off transcriptional switches.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies have used biosensors to engineer expression systems inducible by exogenous substances or to detect molecules in the environment (Dugdale et al, 2013; Garcia-Perez et al, 2022), working as basic on-off transcriptional switches. Nonetheless, only a limited number of studies have employed aTFs, or other synthetic biology devices, to integrate ligand levels into a more complex response, which is key for innovative plant improvements (Vazquez-Vilar et al, 2023). Antunes et al .…”

Section: Discussionmentioning

confidence: 99%

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Genetically encoded Boolean logic operators to sense and integrate phenylpropanoid metabolite levels in plants

Ferreira,

Antunes

2024

Preprint

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SummarySynthetic biology has the potential to revolutionize biotechnology, public health and agriculture. Recent studies have shown the enormous potential of plants as chassis for synthetic biology applications. However, tools to precisely manipulate metabolic pathways for bioproduction in plants are still needed.We have adapted bacterial allosteric transcription factors (aTFs) to control gene expression in plants in a ligand-specific manner. The aTFs used here function as transcription repressors of semi-synthetic promoters, and aTF activity is regulated by specific plant metabolites, especially phenylpropanoid-related molecules. Using these aTFs, we also designed synthetic genetic circuits capable of computing Boolean logic operations.Three aTFs, CouR, FapR and TtgR, were able to achieve ∼95% repression of their respective target promoters. For TtgR, a 6-fold de-repression could be triggered by inducing its ligand (naringenin) accumulation, showing its use as biosensor. Moreover, we designed synthetic genetic circuits that use AND, NAND, IMPLY and NIMPLY Boolean logic operations and integrate metabolite levels as input to the circuit.We showed that biosensors can be implemented in plants to detect phenylpropanoid-related metabolites and activate a genetic circuit that follows a pre-defined logic, demonstrating their potential as tools for exerting control over plant metabolic pathways and facilitating the bioproduction of natural products.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Genetically encoded Boolean logic operators to sense and integrate phenylpropanoid metabolite levels in plants

Ferreira,

Antunes

2024

Preprint

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“…The design of gene and its expression cassette is the first step in getting desired protein in the plant system ( Rozov and Deineko, 2019 ). Proper designing plays a major role in the production of biologics that includes selection of host system, codon optimization, regulatory components associated with foreign gene, host engineering, mode of expression, and purification of biopharmaceuticals ( Webster et al., 2017 ; Peyret et al., 2019 ; Belcher et al., 2020 ; Sainsbury, 2020 ; Hassan et al., 2021 ; Vazquez-Vilar et al., 2023 ). AI-based ML algorithms are proven choice for cost-cutting and efficient designing of product manufacturing in different host systems.…”

Section: Ai-based ML Algorithms In Recombinant Protein Productionmentioning

confidence: 99%

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Parthiban,

Vijeesh,

Gayathri

et al. 2023

Front. Plant Sci.

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Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

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