PRESS RELEASE

Developing a New Approach to Unlocking  Hidden Genetic Potential in Microbes

Scientists in Korea develop iTARGET, a platform designed to identify genetic targets and beneficial gene combinations for microbial strain improvement

Engineering microbes to synthesize medicines, fuels, and valuable chemicals often involves searching for beneficial genetic changes hidden among thousands of genes. A new platform developed by scientists in Korea aims to simplify this challenge by rapidly identifying genetic targets that can enhance microbial performance. By integrating multiple genome-engineering and screening technologies into a single workflow, the approach could accelerate the development of sustainable microbial factories for producing pharmaceuticals, bioplastics, biofuels, flavors, and other bio-based products.

Image title: iTARGET Platform for Rapid Microbial Strain Improvement

Image caption: Novel system uncovers previously hidden genetic targets that can enhance microbial production of sustainable chemicals more efficiently.

Image credit: Gene Drendel from Openverse

Image source link: https://openverse.org/image/9f585169-0e89-40e0-90dc-32d2c70ec15c?q=Escherichia+coli&p=23

License type: CC BY 4.0

Usage restrictions: Credit must be given to the creator.

Microorganisms are increasingly being engineered to manufacture valuable compounds ranging from medicines and food ingredients to biofuels and industrial chemicals. However, turning microbes into efficient production platforms requires extensive strain optimization. Finding the right genetic changes to transform an ordinary microbe into a high-performing producer remains a major challenge, as beneficial genetic targets and gene combinations are often hidden within complex cellular networks.

To address this challenge, Professor Gyoo Yeol Jung from POSTECH, Professor Sungho Jang from Incheon National University, Professor Sang Woo Seo from Seoul National University, and collaborators from UNIST developed iTARGET (integrated Tn-seq and multiplex automated genome engineering (MAGE)-assisted rapid genome engineering targeting), a novel platform designed to rapidly identify genetic modifications that improve microbial performance. Existing methods can generate genetic variants or identify beneficial mutations, but few can efficiently do both while revealing combinations that enhance production. The researchers set out to create a faster and more effective strategy for uncovering these hidden opportunities for strain improvement. Their findings were made available online on November 6, 2025, and published in Volume 44, Issue 3 of the journal Trends in Biotechnology on March 1, 2026.

To demonstrate the capabilities of the platform, the researchers selected naringenin, a plant-derived compound that serves as an important precursor for pharmaceuticals and other bioactive products. The iTARGET workflow integrates multiple technologies into a single, streamlined process. First, random genetic changes were introduced across the microbial genome, while a built-in biosensor selectively enriched cells producing higher levels of naringenin. The enriched population was then analyzed to identify the genetic modifications responsible for the improved production. Next, the most promising targets were combined in different ways, and a fluorescence-based screening system was used to identify the best-performing strains.

The results highlighted the effectiveness of the approach. The initial enrichment process increased naringenin production by 1.7-fold compared with the control population. Further analysis identified ten promising genetic targets, nine of which were experimentally validated to enhance production when individually removed. The most effective single modification boosted naringenin production by 2.3-fold. More importantly, combining two of the identified targets produced an even greater improvement, resulting in a strain that generated 2.8-fold more naringenin than the original microbe. These findings show that iTARGET can uncover both beneficial genetic targets and synergistic combinations that are difficult to predict using conventional approaches.

Beyond improving the production of a single compound, the study showcases a powerful strategy for discovering previously unknown genetic targets that traditional methods may overlook. “The primary application of iTARGET is to rapidly develop and optimize high-performance microbial cell factories for the bio-based production of valuable chemicals. This approach can be leveraged to create low-cost, sustainable, bio-based products, including bioplastics, biofuels, flavors, and pharmaceutical intermediates,” shares Dr. Jang.

Looking ahead, the researchers believe the platform could be adapted to a wide range of microorganisms and products. Commenting on the broader potential of the technology, Dr. Jang notes, “As new engineered biosensors are developed for different chemicals, iTARGET will be able to optimize the production of a vastly expanded spectrum of valuable compounds.”

Overall, iTARGET represents a versatile and powerful platform for microbial strain engineering. By enabling the rapid discovery of novel genetic targets and synergistic gene combinations, it could accelerate the sustainable production of medicines, chemicals, fuels, and other bio-based products.

Reference

About Dr. Sungho Jang

Dr. Sungho Jang is an Associate Professor in the Department of Bioengineering at Incheon National University. His research focuses on redesigning biological systems by integrating principles from synthetic biology and artificial evolution. He develops nucleic acid-based tools, particularly synthetic RNA devices, such as riboswitches, for gene regulation and metabolite sensing. These technologies are applied to chemical and pharmaceutical production, microbiome engineering for therapeutics, and molecular disease diagnostics. Before joining Incheon National University, he completed postdoctoral training in Prof. Ahmad Khalil’s laboratory at Boston University. He earned his Ph.D. in Chemical Engineering from Pohang University of Science and Technology in 2017.