Multigene engineering in plants: Technologies, applications, and future prospects

Ruchika Rajput; Brandon A. Boone; Rushil Mandlik; Md Torikul Islam; Yiping Qi; Jack Wang; Rodolphe Barrangou; Rosangela Sozzani; Carrie A. Eckert; Gerald A. Tuskan; Jin Gui Chen; Xiaohan Yang

doi:10.1016/j.biotechadv.2025.108697

Multigene engineering in plants: Technologies, applications, and future prospects

Ruchika Rajput
, Brandon A. Boone
, Rushil Mandlik
, Md Torikul Islam
, Yiping Qi
, Jack Wang
, Rodolphe Barrangou
, Rosangela Sozzani
, Carrie A. Eckert
, Gerald A. Tuskan
, Jin Gui Chen
, Xiaohan Yang

Research output: Contribution to journal › Review article › peer-review

Abstract

The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

Original language	English
Article number	108697
Journal	Biotechnology Advances
Volume	85
DOIs	https://doi.org/10.1016/j.biotechadv.2025.108697
State	Published - Dec 2025

Funding

The writing of this manuscript was supported by the Center for Bioenergy Innovation (under FWP ERKP886 ), a U.S. Department of Energy (DOE) Bioenergy Research Center supported by the Biological and Environmental Research (BER) program. Oak Ridge National Laboratory is managed by UT-Battelle, LLC for the U.S. DOE under Contract Number DE-AC05-00OR22725 . YQ acknowledges funding support from NSF (Grant no. IOS-2029889 and IOS-2132693 ), USDA-NIFA (Grant no. 2023-67013-39628 and 2024-33522-42755 ), and FFAR (Grant no. 21010111 ). JW acknowledges funding support from the Cooperative State Research Service of the USDA grant NCZ04214 . RS acknowledges funding support from NSF CBET-2019435 (STC: Science and Technologies for Phosphorus Sustainability Center). Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Keywords

AI-aided plant engineering
Crop biofactories
DBTL cycle
Gene stacking
Metabolic pathway engineering
Synthetic biology

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10.1016/j.biotechadv.2025.108697

Cite this

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title = "Multigene engineering in plants: Technologies, applications, and future prospects",

abstract = "The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.",

keywords = "AI-aided plant engineering, Crop biofactories, DBTL cycle, Gene stacking, Metabolic pathway engineering, Synthetic biology",

author = "Ruchika Rajput and Boone, \{Brandon A.\} and Rushil Mandlik and Islam, \{Md Torikul\} and Yiping Qi and Jack Wang and Rodolphe Barrangou and Rosangela Sozzani and Eckert, \{Carrie A.\} and Tuskan, \{Gerald A.\} and Chen, \{Jin Gui\} and Xiaohan Yang",

note = "Publisher Copyright: {\textcopyright} 2025 The Authors",

year = "2025",

month = dec,

doi = "10.1016/j.biotechadv.2025.108697",

language = "English",

volume = "85",

journal = "Biotechnology Advances",

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T2 - Technologies, applications, and future prospects

AU - Rajput, Ruchika

AU - Boone, Brandon A.

AU - Mandlik, Rushil

AU - Islam, Md Torikul

AU - Qi, Yiping

AU - Wang, Jack

AU - Barrangou, Rodolphe

AU - Sozzani, Rosangela

AU - Eckert, Carrie A.

AU - Tuskan, Gerald A.

AU - Chen, Jin Gui

AU - Yang, Xiaohan

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N2 - The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

AB - The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

KW - AI-aided plant engineering

KW - Crop biofactories

KW - DBTL cycle

KW - Gene stacking

KW - Metabolic pathway engineering

KW - Synthetic biology

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U2 - 10.1016/j.biotechadv.2025.108697

DO - 10.1016/j.biotechadv.2025.108697

M3 - Review article

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AN - SCOPUS:105014921378

SN - 0734-9750

VL - 85

JO - Biotechnology Advances

JF - Biotechnology Advances

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ER -

Multigene engineering in plants: Technologies, applications, and future prospects

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Keywords

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