ArticleNature plants2025
Quantitative dissection of Agrobacterium T-DNA expression in single plant cells reveals density-dependent synergy and antagonism.
Article in Nature plants, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Bacteriophage P22 Virus-Like Particles as Nanoscale Protein Scaffolds for Plant Synthetic Biology.Plant biotechnology journal · 2026Article
- Polyphenol oxidase depletion in Nicotiana benthamiana enhances recombinant protein purification and preserves native protein integrity.The New phytologist · 2026Article
- Rational design of T-DNA vectors enables predictable, single-copy integration inbioRxiv : the preprint server for biology · 2026Article
- Causes and consequences of experimental variation in Nicotiana benthamiana transient expression.Nature communications · 2026Article
- Engineering Conditional Transgene Expression in Nicotiana benthamiana.Plant biotechnology journal · 2026Review
- Protocol for triggering plant gene silencing by viral delivery of short RNA.STAR protocols · 2025Article
- Advances and prospects of large DNA fragment editing in plants.Nature plants · 2025Review
- Multiplexed profiling of transcriptional regulators in plant cells.Nature biotechnology · 2025Article
- Cooperation and antagonism in Agrobacterium-mediated transformation.Nature plants · 2025Article
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Authors and funding
13 authors.
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Abstract
Agrobacterium pathogenesis, which involves transferring T-DNA into plant cells, is the cornerstone of plant genetic engineering. As the applications that rely on Agrobacterium increase in sophistication, it becomes critical to achieve a quantitative and predictive understanding of T-DNA expression at the level of single plant cells. Here we examine if a classic Poisson model of interactions between pathogens and host cells holds true for Agrobacterium infecting Nicotiana benthamiana. Systematically challenging this model revealed antagonistic and synergistic density-dependent interactions between bacteria that do not require quorum sensing. Using various approaches, we studied the molecular basis of these interactions. To overcome the engineering constraints imposed by antagonism, we created a dual binary vector system termed 'BiBi', which can improve the efficiency of a reconstituted complex metabolic pathway in a predictive fashion. Our findings illustrate how combining theoretical models with quantitative experiments can reveal new principles of bacterial pathogenesis, impacting both fundamental and applied plant biology.
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