Engineering plant immune circuit: walking to the bright future with a novel toolbox

Vuong, Uyen Thi; Iswanto, Arya Bagus Boedi; Nguyen, Quang‐Minh; Kang, Hobin; Lee, Ji‐Hyun; Moon, Ji‐Yun; Kim, Sang Hee

doi:10.1111/pbi.13916

Cited by 11 publications

(5 citation statements)

References 405 publications

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“…Additionally, such genetic resistance has proven relatively short lived when deployed at scale [54], although considerable effort is being put into stacking immune receptors to increase efficacy in the field [55][56][57]. As plant sciences and crop improvement move to engineered systems approaches [58][59][60], greater understanding and predictive power for processes such as plant immunity, abiotic stress tolerance, growth, and nutrient utilization are needed. Systems approaches leveraging increasingly common big data sets, analyzed by interdisciplinary teams, can help provide the knowledge and framework to achieve these goals.…”

Section: Discussionmentioning

confidence: 99%

Machine learning general transcriptional predictors of plant disease

Sia,

Zhang,

Cheng

et al. 2023

Preprint

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Plants utilize an innate immune system to defend against all classes of microbial invaders. While we understand specific genetic determinants of host-pathogen interactions, it remains less clear how generalized the immune response is to diverse pathogens. Using a data-driven approach, and utilizing feature selection based on network science and topology, we developed machine learning models that could predict host disease development across diverse pathosystems. These machine learning models identified early transcriptional responses predictive of later disease development, regardless of pathogen class, using a fraction of the host transcriptome. The identified gene sets were not enriched for canonical defense genes, but where statistically enriched for genes previously identified from independent data sets, including those described as representing a general plant stress response. These results highlight novel components of a general plant immune response, and demonstrate the application of machine learning to address biological hypotheses of a complex multigenic outcome.

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Section: Discussionmentioning

confidence: 99%

Machine learning general transcriptional predictors of plant disease

Sia,

Zhang,

Cheng

et al. 2023

Preprint

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“…An important aspect in the development of transgene‐based, pathogen‐resistant crops is that the respective transgene should have no adverse effect on plant performance. Typically, however, resistance‐conferring transgenic systems are based on one or more constitutively expressed components, such as an effector‐specific receptor protein (Vuong et al ., 2022 ). This not only places an energetic burden on the plant but also carries the risk that constitutive expression will become the target of host gene silencing, ultimately resulting in a lack of receptor expression and a dysfunctional perception system (Rajeevkumar et al ., 2015 ).…”

Section: Discussionmentioning

confidence: 99%

An ancient cis‐element targeted by Ralstonia solanacearum TALE‐like effectors facilitates the development of a promoter trap that could confer broad‐spectrum wilt resistance

Gallas,

Li,

von Roepenack‐Lahaye

et al. 2023

Plant Biotechnology Journal

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SummaryRalstonia solanacearum, a species complex of bacterial plant pathogens that causes bacterial wilt, comprises four phylotypes that evolved when a founder population was split during the continental drift ~180 million years ago. Each phylotype contains strains with RipTAL proteins structurally related to transcription activator‐like (TAL) effectors from the bacterial pathogen Xanthomonas. RipTALs have evolved in geographically separated phylotypes and therefore differ in sequence and potentially functionality. Earlier work has shown that phylotype I RipTAL Brg11 targets a 17‐nucleotide effector binding element (EBE) and transcriptionally activates the downstream arginine decarboxylase (ADC) gene. The predicted DNA binding preferences of Brg11 and RipTALs from other phylotypes are similar, suggesting that most, if not all, RipTALs target the Brg11‐EBE motif and activate downstream ADC genes. Here we show that not only phylotype I RipTAL Brg11 but also RipTALs from other phylotypes activate host genes when preceded by the Brg11‐EBE motif. Furthermore, we show that Brg11 and RipTALs from other phylotypes induce the same quantitative changes of ADC‐dependent plant metabolites, suggesting that most, if not all, RipTALs induce functionally equivalent changes in host cells. Finally, we report transgenic tobacco lines in which the RipTAL‐binding motif Brg11‐EBE mediates RipTAL‐dependent transcription of the executor‐type resistance (R) gene Bs4C from pepper, thereby conferring resistance to RipTAL‐delivering R. solanacearum strains. Our results suggest that cell death‐inducing executor‐type R genes, preceded by the RipTAL‐binding motif Brg11‐EBE, could be used to genetically engineer broad‐spectrum bacterial wilt resistance in crop plants without any apparent fitness penalty.

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“…However, the underlying mechanisms of post-elicitor recognition are far from being understood. One way to expand the research field of LecRLKs could be to apply novel genome editing and silencing technologies, thereby modifying the carbohydrate-binding properties of LecRLKs and other PRRs in planta ( Vuong, 2022 ). This could yield increased knowledge about biotic interactions and potential agronomic applications.…”

Section: Discussionmentioning

confidence: 99%