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Why Do Plants Attack Themselves? The Secret of Genetic Conflict Revealed
<Professor Ji-Joon Song of the KAIST Department of Biological Sciences>
Plants, with their unique immune systems, sometimes launch 'autoimmune responses' by mistakenly identifying their own protein structures as pathogens. In particular, 'hybrid necrosis,' a phenomenon where descendant plants fail to grow healthily and perish after cross-breeding different varieties, has long been a difficult challenge for botanists and agricultural researchers. In response, an international research team has successfully elucidated the mechanism inducing plant autoimmune responses and proposed a novel strategy for cultivar improvement that can predict and avoid these reactions.
Professor Ji-Joon Song's research team at KAIST, in collaboration with teams from the National University of Singapore (NUS) and the University of Oxford, announced on the 21st of July that they have elucidated the structure and function of the 'DM3' protein complex, which triggers plant autoimmune responses, using cryo-electron microscopy (Cryo-EM) technology.
This research is drawing attention because it identifies defects in protein structure as the cause of hybrid necrosis, which occurs due to an abnormal reaction of immune receptors during cross-breeding between plant hybrids.
This protein (DM3) is originally an enzyme involved in the plant's immune response, but problems arise when the structure of the DM3 protein is damaged in a specific protein combination called 'DANGEROUS MIX (DM)'.
Notably, one variant of DM3, the 'DM3Col-0' variant, forms a stable complex with six proteins and is recognized as normal, thus not triggering an immune response. In contrast, another 'DM3Hh-0' variant has improper binding between its six proteins, causing the plant to recognize it as an 'abnormal state' and trigger an immune alarm, leading to autoimmunity.
The research team visualized this structure using atomic-resolution cryo-electron microscopy (Cryo-EM) and revealed that the immune-inducing ability is not due to the enzymatic function of the DM3 protein, but rather to 'differences in protein binding affinity.'
This demonstrates that plants can initiate an immune response by recognizing not only 'external pathogens' but also 'internal protein structures' when they undergo abnormal changes, treating them as if they were pathogens.
The study shows how sensitively the plant immune system changes and triggers autoimmune responses when genes are mixed and protein structures change during the cross-breeding of different plant varieties. It significantly advanced the understanding of genetic incompatibility that can occur during natural cross-breeding and cultivar improvement processes.
Dr. Gijeong Kim, the co-first author, stated, "Through international research collaboration, we presented a new perspective on understanding the plant immune system by leveraging the autoimmune phenomenon, completing a high-quality study that encompasses structural biochemistry, genetics, and cell biological experiments."
Professor Ji-Joon Song of the KAIST Department of Biological Sciences, who led the research, said, "The fact that the immune system can detect not only external pathogens but also structural abnormalities in its own proteins will set a new standard for plant biotechnology and crop breeding strategies. Cryo-electron microscopy-based structural analysis will be an important tool for understanding the essence of gene interactions."
This research, with Professor Ji-Joon Song and Professor Eunyoung Chae of the University of Oxford as co-corresponding authors, Dr. Gijeong Kim (currently a postdoctoral researcher at the University of Zurich) and Dr. Wei-Lin Wan of the National University of Singapore as co-first authors, and Ph.D candidate Nayun Kim, as the second author, was published on July 17th in Molecular Cell, a sister journal of the international academic journal Cell.
This research was supported by the KAIST Grand Challenge 30 project.
Article Title: Structural determinants of DANGEROUS MIX 3, an alpha/beta hydrolase that triggers NLR-mediated genetic incompatibility in plants DOI: https://doi.org/10.1016/j.molcel.2025.06.021
2025.07.21 View 40 -
KAIST Develops New AI Inference-Scaling Method for Planning
<(From Left) Professor Sungjin Ahn, Ph.D candidate Jaesik Yoon, M.S candidate Hyeonseo Cho, M.S candidate Doojin Baek, Professor Yoshua Bengio>
<Ph.D candidate Jaesik Yoon from professor Ahn's research team>
Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability*, particularly for reasoning and planning (known as System 2 abilities) has been lacking. In response, the research team has developed a new technology that enables high-performance and efficient inference for planning based on diffusion models. This technology demonstrated its performance by achieving a 100% success rate on an giant maze-solving task that no existing model had succeeded in. The results are expected to serve as core technology in various fields requiring real-time decision-making, such as intelligent robotics and real-time generative AI.
*Inference-time scalability: Refers to an AI model’s ability to flexibly adjust performance based on the computational resources available during inference.
KAIST (President Kwang Hyung Lee) announced on the 20th that a research team led by Professor Sungjin Ahn in the School of Computing has developed a new technology that significantly improves the inference-time scalability of diffusion-based reasoning through joint research with Professor Yoshua Bengio of the University of Montreal, a world-renowned scholar in deep learning. This study was carried out as part of a collaboration between KAIST and Mila (Quebec AI Institute) through the Prefrontal AI Joint Research Center.
This technology is gaining attention as a core AI technology that, after training, allows the AI to efficiently utilize more computational resources during inference to solve complex reasoning and planning problems that cannot be addressed merely by scaling up data or model size. However, current diffusion models used across various applications lack effective methodologies for implementing such scalability particularly for reasoning and planning.
To address this, Professor Ahn’s research team collaborated with Professor Bengio to propose a novel diffusion model inference technique based on Monte Carlo Tree Search. This method explores diverse generation paths during the diffusion process in a tree structure and is designed to efficiently identify high-quality outputs even with limited computational resources. As a result, it achieved a 100% success rate on the "giant-scale maze-solving" task, where previous methods had a 0% success rate.
In the follow-up research, the team also succeeded in significantly improving the major drawback of the proposed method—its slow speed. By efficiently parallelizing the tree search and optimizing computational cost, they achieved results of equal or superior quality up to 100 times faster than the previous version. This is highly meaningful as it demonstrates the method’s inference capabilities and real-time applicability simultaneously.
Professor Sungjin Ahn stated, “This research fundamentally overcomes the limitations of existing planning method based on diffusion models, which required high computational cost,” adding, “It can serve as core technology in various areas such as intelligent robotics, simulation-based decision-making, and real-time generative AI.”
<Photo taken at the International Conference>
The research results were presented as Spotlight papers (top 2.6% of all accepted papers) by doctoral student Jaesik Yoon of the School of Computing at the 42nd International Conference on Machine Learning (ICML 2025), held in Vancouver, Canada, from July 13 to 19.
※ Paper titles: Monte Carlo Tree Diffusion for System 2 Planning (Jaesik Yoon, Hyeonseo Cho, Doojin Baek, Yoshua Bengio, Sungjin Ahn, ICML 25), Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning (Jaesik Yoon, Hyeonseo Cho, Yoshua Bengio, Sungjin Ahn)
※ DOI: https://doi.org/10.48550/arXiv.2502.07202, https://doi.org/10.48550/arXiv.2506.09498
This research was supported by the National Research Foundation of Korea.
2025.07.21 View 49 -
KAIST's Im Mi-hee wins Korea's L'Oréal-UNESCO Women in Science Award
<At the 24th L'Oréal-UNESCO For Women in Science Awards ceremony, winners and officials take a commemorative photo. From left: Yoon Byeong-soon, Acting Secretary General of the Korean National Commission for UNESCO; Hwang Eun-sook, Chairperson of the Women’s Bioscience Forum; Lim Mi-hee, Professor at the Korea Advanced Institute of Science and Technology (KAIST) and recipient of the Academic Promotion Award; Kang Mi-kyung, Assistant Professor at Korea University and recipient of the Fellowship Award; Lee Jeong-hyun, Assistant Professor at Kongju National University; Jeon Ji-hye, Assistant Professor at Gyeongsang National University; Jo Yu-na, Research Professor at Pusan National University; and Samuel du Retail, CEO of L'Oréal Korea./Courtesy of L'Oréal Korea>
Im Mi-hee, a professor at the Korea Advanced Institute of Science and Technology (KAIST) Department of Chemistry, received the Academic Promotion Award at the 24th Korean L'Oréal-UNESCO Women in Science Awards ceremony.
L'Oréal Korea, the Korean National Commission for UNESCO, and the Women’s Bioscience Forum held the 24th Korean L'Oréal-UNESCO Women in Science Awards ceremony on the 16th and noted that Im Mi-hee was selected for this year’s Academic Promotion Award.
Professor Im was recognized for her research on the causes of Alzheimer's disease at the molecular level and her efforts in the discovery of intracellular proteins that promote the toxicity of Alzheimer’s-inducing factors. Professor Im is a full member of the Korean Academy of Science and Technology (KAST) and has received several awards including the Hanseong Science Award, this year's Women in Science and Technology Award, and the RIGAKU-ACCC Award (Asia's top woman scientist).
The fellowship section, awarded to four emerging women scientists, includes Kang Mi-kyung, an assistant professor at Korea University’s Department of Health and Environmental Sciences; Jeon Ji-hye, an assistant professor at Gyeongsang National University’s Department of Life Sciences; Jo Yu-na, a research professor at Pusan National University’s College of Medicine; and Lee Jeong-hyun, an assistant professor at Kongju National University’s Department of Environmental Education.
The recipients of the Academic Promotion Award and fellowships will receive a certificate and a trophy, along with research funding of 30 million won and 7 million won, respectively.
Samuel du Retail, the representative of L'Oréal Korea, said, “The L'Oréal Group continues to support the empowerment of women scientists and the improvement of research environments worldwide under the philosophy that 'the world needs science, and science needs women.' We will actively support more female talents to shine at the center of scientific and technological advancement in the future.”
2025.07.18 View 98 -
KAIST Holds '2025 KAIST Science Frontier Camp' for Multicultural Youth
<2025 KAIST Science Frontier Camp Activities>
KAIST (President Kwang Hyung Lee) announced on the 18th of July that it hosted the '2025 KAIST Science Frontier Camp' for multicultural youth from the 15th for three days and two nights at the Creative Learning Building on its main campus in Daejeon.
This event was organized in accordance with the 'Multicultural Talent Nurturing Agreement' signed by KAIST and GS Caltex in 2024. It marks the first year of a mid-to-long-term project in which 100 million KRW in development funds will be contributed annually for four years. The Global Institute for Talented Education organized the camp, and approximately 30 middle school students from multicultural families affiliated with the 'Hanmaum Educational Volunteer Group' (Director, Honorary Professor Byung Kyu Choi), a mentoring and volunteer organization for multicultural students, participated.
The camp participants enjoyed developing their scientific thinking skills and problem-solving abilities, and broadening their understanding of STEM (Science, Technology, Engineering, and Mathematics) career paths through a variety of science activity programs, including: △'Black Box: Record the Egg's Last Moment!' △'Find the Best Strategy! Heuristic Algorithm Challenge' △'Future Society and AI, Finding Career Directions' △'Distance Dominates the World!' and △'Career Talk Concert.'
During the opening ceremony, Director Byung Kyu Choi delivered a congratulatory speech. Additionally, Yong Hyun Kim, Dean of Admissions at KAIST, gave a special lecture titled 'La La Land KAIST – A Story of Chasing the Dream of a Young Scientist,' sharing honest stories about careers and dreams as a scientist.
Gi Jung Yoo, a freshman from the Division of Undeclared Majors who participated in the camp as a student mentor, shared that he had a very meaningful time mentoring the participating students, who are future STEM hopefuls, sharing vivid experiences as well as insights on metric functions. He added his hope that more students would be given such opportunities.
< Students Actively Taking Part in the Camp Activities>
Si Jong Kwak, Director of the Global Institute for Talented Education, stated, "We hope this will be a practical way to help students foster their interest in science, learn the joy of discussion and communication, and design their future."
KAIST President Kwang Hyung Lee remarked, "This camp was a valuable opportunity for students from diverse cultural backgrounds to gain confidence through science and envision their future." He added, "KAIST will continue to dedicate efforts to nurturing multicultural talent and contribute to creating a sustainable society."
Since 2024, KAIST has introduced and selected multicultural students through its Equal Opportunity Admission track. Utilizing the development funds from GS Caltex, KAIST also established the 'GS Caltex Multicultural Excellence Scholarship Program.' Through this scholarship program, undergraduate students from multicultural families receive living expenses each semester, allowing them to focus more stably on their studies. As the number of applicants for the Equal Opportunity Admission track is increasing every year, more multicultural students are expected to benefit from scholarships in the future.
Additionally, in May, both organizations invited Ms. Si Si Wu Fong, a foreign employee at GS Caltex, to give a special lecture titled 'Working Life for Foreigners in Korea' to support foreign students' career exploration. Foreign students who attended the lecture reported positive feedback, stating that they gained practical career information and were motivated to pursue employment in STEM fields in Korea.
KAIST plans to continue strengthening its efforts to nurture multicultural talent, increase understanding of the upcoming multicultural society, and help spread social values.
<At the 2025 KAIST Science Frontier Camp>
2025.07.18 View 141 -
KAIST reveals for the first time the mechanism by which alcohol triggers liver inflammation
<(From left)Dr. Keungmo Yang, Professor Won-Il Jeong, Ph.D candidate Kyurae Kim>
Excessive alcohol consumption causes alcoholic liver disease, and about 20% of these cases progress to alcohol-associated steatohepatitis (ASH), which can lead to liver cirrhosis and liver failure. Early diagnosis and treatment are therefore extremely important. A KAIST research team has identified a new molecular mechanism in which alcohol-damaged liver cells increase reactive oxygen species (ROS), leading to cell death and inflammatory responses. In addition, they discovered that Kupffer cells, immune cells residing in the liver, act as a “dual-function regulator” that can either promote or suppress inflammation through interactions with liver cells.
KAIST (President Kwang-Hyung Lee) announced on the 17th that a research team led by Professor Won-Il Jeong from the Graduate School of Medical Science and Engineering, in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center, has uncovered the molecular pathway of liver damage and inflammation caused by alcohol consumption. This finding offers new clues for the diagnosis and treatment of alcohol-associated liver disease (ALD).
Professor Won-Il Jeong’s research team found that during chronic alcohol intake, expression of the vesicular glutamate transporter VGLUT3 increases, leading to glutamate accumulation in hepatocytes. Subsequent binge drinking causes rapid changes in intracellular calcium levels, which then triggers glutamate* secretion. The secreted glutamate stimulates the glutamate receptor mGluR5 on liver-resident macrophages (Kupffer cells), which induces ROS production and activates a pathological pathway resulting in hepatocyte death and inflammation.
*Glutamate: A type of amino acid involved in intercellular signaling, protein synthesis, and energy metabolism in various tissues including the brain and liver. In excess, it can cause overexcitation and death of nerve cells.
<Figure1. Glutamate accumulation in perivenous hepatocytes through vesicular glutamate transporter 3 after 2-week EtOH intake and its release by binge drinking>
A particularly groundbreaking aspect of this study is that damaged hepatocytes and Kupffer cells can form a "pseudosynapse"—a structure similar to a synapse which is previously thought to occur only in the brain—enabling them to exchange signals. This is the first time such a phenomenon has been identified in the liver.
This pseudosynapse forms when hepatocytes expand (ballooning) due to alcohol, becoming physically attached to Kupffer cells. Simply put, the damaged hepatocytes don’t just die—they send distress signals to nearby immune cells, prompting a response.
This discovery proposes a new paradigm: even in peripheral organs, direct structural contact between cells can allow signal transmission. It also shows that damaged hepatocytes can actively stimulate macrophages and induce regeneration through cell death, revealing the liver’s “autonomous recovery function.”
The team also confirmed in animal models that genetic or pharmacological inhibition of VGLUT3, mGluR5, or the ROS-producing enzyme NOX2 reduces alcohol-induced liver damage. They also confirmed that the same mechanism observed in animal models was present in human patients with ALD by analyzing blood and liver tissue samples.
<Figure2. Binge drinking rapidly alters the intracellular calcium levels to release glutamates and activate mGluR5 of Kupffer cells>
Professor Won-Il Jeong of KAIST said, “These findings may serve as new molecular targets for early diagnosis and treatment of ASH in the future.”
This study was jointly led by Dr. Keungmo Yang (now at Yeouido St. Mary’s Hospital) and Kyurae Kim, a doctoral candidate at KAIST, who served as co–first authors. It was conducted in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center and was published in the journal Nature Communications on July 1.
※ Article Title: Binge drinking triggers VGLUT3-mediated glutamate secretion and subsequent hepatic inflammation by activating mGluR5/NOX2 in Kupffer cells ※ DOI: https://doi.org/10.1038/s41467-025-60820-3
This study was supported by the Ministry of Science and ICT through the National Research Foundation of Korea's Global Leader Program, Mid-Career Researcher Program, and the Bio & Medical Technology Development Program.
2025.07.17 View 214 -
KAIST Successfully Implements 3D Brain-Mimicking Platform with 6x Higher Precision
<(From left) Dr. Dongjo Yoon, Professor Je-Kyun Park from the Department of Bio and Brain Engineering, (upper right) Professor Yoonkey Nam, Dr. Soo Jee Kim>
Existing three-dimensional (3D) neuronal culture technology has limitations in brain research due to the difficulty of precisely replicating the brain's complex multilayered structure and the lack of a platform that can simultaneously analyze both structure and function. A KAIST research team has successfully developed an integrated platform that can implement brain-like layered neuronal structures using 3D printing technology and precisely measure neuronal activity within them.
KAIST (President Kwang Hyung Lee) announced on the 16th of July that a joint research team led by Professors Je-Kyun Park and Yoonkey Nam from the Department of Bio and Brain Engineering has developed an integrated platform capable of fabricating high-resolution 3D multilayer neuronal networks using low-viscosity natural hydrogels with mechanical properties similar to brain tissue, and simultaneously analyzing their structural and functional connectivity.
Conventional bioprinting technology uses high-viscosity bioinks for structural stability, but this limits neuronal proliferation and neurite growth. Conversely, neural cell-friendly low-viscosity hydrogels are difficult to precisely pattern, leading to a fundamental trade-off between structural stability and biological function. The research team completed a sophisticated and stable brain-mimicking platform by combining three key technologies that enable the precise creation of brain structure with dilute gels, accurate alignment between layers, and simultaneous observation of neuronal activity.
The three core technologies are: ▲ 'Capillary Pinning Effect' technology, which enables the dilute gel (hydrogel) to adhere firmly to a stainless steel mesh (micromesh) to prevent it from flowing, thereby reproducing brain structures with six times greater precision (resolution of 500 μm or less) than conventional methods; ▲ the '3D Printing Aligner,' a cylindrical design that ensures the printed layers are precisely stacked without misalignment, guaranteeing the accurate assembly of multilayer structures and stable integration with microelectrode chips; and ▲ 'Dual-mode Analysis System' technology, which simultaneously measures electrical signals from below and observes cell activity with light (calcium imaging) from above, allowing for the simultaneous verification of the functional operation of interlayer connections through multiple methods.
< Figure 1. Platform integrating brain-structure-mimicking neural network model construction and functional measurement technology>
The research team successfully implemented a three-layered mini-brain structure using 3D printing with a fibrin hydrogel, which has elastic properties similar to those of the brain, and experimentally verified the process of actual neural cells transmitting and receiving signals within it.
Cortical neurons were placed in the upper and lower layers, while the middle layer was left empty but designed to allow neurons to penetrate and connect through it. Electrical signals were measured from the lower layer using a microsensor (electrode chip), and cell activity was observed from the upper layer using light (calcium imaging). The results showed that when electrical stimulation was applied, neural cells in both upper and lower layers responded simultaneously. When a synapse-blocking agent (synaptic blocker) was introduced, the response decreased, proving that the neural cells were genuinely connected and transmitting signals.
Professor Je-Kyun Park of KAIST explained, "This research is a joint development achievement of an integrated platform that can simultaneously reproduce the complex multilayered structure and function of brain tissue. Compared to existing technologies where signal measurement was impossible for more than 14 days, this platform maintains a stable microelectrode chip interface for over 27 days, allowing the real-time analysis of structure-function relationships. It can be utilized in various brain research fields such as neurological disease modeling, brain function research, neurotoxicity assessment, and neuroprotective drug screening in the future."
<Figure 2. Integration process of stacked bioprinting technology and microelectrode chip>
The research, in which Dr. Soo Jee Kim and Dr. Dongjo Yoon from KAIST's Department of Bio and Brain Engineering participated as co-first authors, was published online in the international journal 'Biosensors and Bioelectronics' on June 11, 2025.
※Paper: Hybrid biofabrication of multilayered 3D neuronal networks with structural and functional interlayer connectivity
※DOI: https://doi.org/10.1016/j.bios.2025.117688
2025.07.16 View 219 -
KAIST Develops Robots That React to Danger Like Humans
<(From left) Ph.D candidate See-On Park, Professor Jongwon Lee, and Professor Shinhyun Choi>
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
KAIST (President Kwang Hyung Lee) announced on July15th that a joint research team led by Endowed Chair Professor Shinhyun Choi of the School of Electrical Engineering at KAIST and Professor Jongwon Lee of the Department of Semiconductor Convergence at Chungnam National University (President Jung Kyum Kim) developed a next-generation neuromorphic semiconductor-based artificial sensory nervous system. This system mimics the functions of a living organism's sensory nervous system, and enables a new type of robotic system that can efficiently responds to external stimuli.
In nature, animals — including humans — ignore safe or familiar stimuli and selectively react sensitively to important or dangerous ones. This selective response helps prevent unnecessary energy consumption while maintaining rapid awareness of critical signals. For instance, the sound of an air conditioner or the feel of clothing against the skin soon become familiar and are disregarded. However, if someone calls your name or a sharp object touches your skin, a rapid focus and response occur. These behaviors are regulated by the 'habituation' and 'sensitization' functions in the sensory nervous system. Attempts have been consistently made to apply these sensory nervous system functions of living organisms in order to create robots that efficiently respond to external environments like humans.
However, implementing complex neural characteristics such as habituation and sensitization in robots has faced difficulties in miniaturization and energy efficiency due to the need for separate software or complex circuitry. In particular, there have been attempts to utilize memristors, a neuromorphic semiconductor. A memristor is a next-generation electrical device, which has been widely utilized as an artificial synapse due to its ability to store analog value in the form of device resistance. However, existing memristors had limitations in mimicking the complex characteristics of the nervous system because they only allowed simple monotonic changes in conductivity.
To overcome these limitations, the research team developed a new memristor capable of reproducing complex neural response patterns such as habituation and sensitization within a single device. By introducing additional layer inside the memristor that alter conductivity in opposite directions, the device can more realistically emulate the dynamic synaptic behaviors of a real nervous system — for example, decreasing its response to repeated safe stimuli but quickly regaining sensitivity when a danger signal is detected.
<New memristor mimicking functions of sensory nervous system such as habituation/sensitization>
Using this new memristor, the research team built an artificial sensory nervous system capable of recognizing touch and pain, an applied it to a robotic hand to test its performance. When safe tactile stimuli were repeatedly applied, the robot hand, which initially reacted sensitively to unfamiliar tactile stimuli, gradually showed habituation characteristics by ignoring the stimuli. Later, when stimuli were applied along with an electric shock, it recognized this as a danger signal and showed sensitization characteristics by reacting sensitively again. Through this, it was experimentally proven that robots can efficiently respond to stimuli like humans without separate complex software or processors, verifying the possibility of developing energy-efficient neuro-inspired robots.
<Robot arm with memristor-based artificial sensory nervous system>
See-On Park, researcher at KAIST, stated, "By mimicking the human sensory nervous system with next-generation semiconductors, we have opened up the possibility of implementing a new concept of robots that are smarter and more energy-efficient in responding to external environments." He added, "This technology is expected to be utilized in various fusion fields of next-generation semiconductors and robotics, such as ultra-small robots, military robots, and medical robots like robotic prosthetics".
This research was published online on July 1st in the international journal 'Nature Communications,' with Ph.D candidate See-On Park as the first author.
Paper Title: Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics
DOI: https://doi.org/10.1038/s41467-025-60818-x
This research was supported by the Korea National Research Foundation's Next-Generation Intelligent Semiconductor Technology Development Project, the Mid-Career Researcher Program, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the Excellent New Researcher Program, and the Nano Convergence Technology Division, National Nanofab Center's (NNFC) Nano-Medical Device Project.
2025.07.16 View 263 -
A KAIST Team Engineers a Microbial Platform for Efficient Lutein Production
<(From Left) Ph.D. Candidate Hyunmin Eun, Distinguished Professor Sang Yup Lee, , Dr. Cindy Pricilia Surya Prabowo>
The application of systems metabolic engineering strategies, along with the construction of an electron channeling system, has enabled the first gram-per-liter scale production of lutein from Corynebacterium glutamicum, providing a viable alternative to plant-derived lutein production.
A research group at KAIST has successfully engineered a microbial strain capable of producing lutein at industrially relevant levels. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, developed a novel C. glutamicum strain using systems metabolic engineering strategies to overcome the limitations of previous microbial lutein production efforts. This research is expected to be beneficial for the efficient production of other industrially important natural products used in food, pharmaceuticals, and cosmetics.
Lutein is a xanthophyll carotenoid found in egg yolk, fruits, and vegetables, known for its role in protecting our eyes from oxidative stress and reducing the risk of macular degeneration and cataracts. Currently, commercial lutein is predominantly extracted from marigold flowers; however, this approach has several drawbacks, including long cultivation times, high labor costs, and inefficient extraction yields, making it economically unfeasible for large-scale production. These challenges have driven the demand for alternative production methods.
To address these issues, KAIST researchers, including Ph.D. Candidate Hyunmin Eun, Dr. Cindy Pricilia Surya Prabowo, and Distinguished Professor Sang Yup Lee, applied systems metabolic engineering strategies to engineer C. glutamicum, a GRAS (Generally Recognized As Safe) microorganism widely used in industrial fermentation. Unlike Escherichia coli, which was previously explored for microbial lutein production, C. glutamicum lacks endotoxins, making it a safer and more viable option for food and pharmaceutical applications.
The team’s work, entitled “Gram-per-litre scale production of lutein by engineered Corynebacterium,” was published in Nature Synthesis on 04 July , 2025.
This research details the high-level production of lutein using glucose as a renewable carbon source via systems metabolic engineering. The team focused on eliminating metabolic bottlenecks that previously limited microbial lutein synthesis. By employing enzyme scaffold-based electron channeling strategies, the researchers improved metabolic flux towards lutein biosynthesis while minimizing unwanted byproducts.
<Lutein production metabolic pathway engineering>
To enhance productivity, bottleneck enzymes within the metabolic pathway were identified and optimized. It was determined that electron-requiring cytochrome P450 enzymes played a major role in limiting lutein biosynthesis. To overcome this limitation, an electron channeling strategy was implemented, where engineered cytochrome P450 enzymes and their reductase partners were spatially organized on synthetic scaffolds, allowing more efficient electron transfer and significantly increasing lutein production.
The engineered C. glutamicum strain was further optimized in fed-batch fermentation, achieving a record-breaking 1.78 g/L of lutein production within 54 hours, with a content of 19.51 mg/gDCW and a productivity of 32.88 mg/L/h—the highest lutein production performance in any host reported to date. This milestone demonstrates the feasibility of replacing plant-based lutein extraction with microbial fermentation technology.
“We can anticipate that this microbial cell factory-based mass production of lutein will be able to replace the current plant extraction-based process,” said Ph.D. Candidate Hyunmin Eun. He emphasized that the integrated metabolic engineering strategies developed in this study could be broadly applied for the efficient production of other valuable natural products used in pharmaceuticals and nutraceuticals.
<Schematic diagram of microbial-based lutein production platform>
“As maintaining good health in an aging society becomes increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other medically and nutritionally significant natural products,” added Distinguished Professor Sang Yup Lee.
This work is supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project 2022M3J5A1056072 and the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries project 2022M3J5A1056117 from the National Research Foundation supported by the Korean Ministry of Science and ICT.
Source:
Hyunmin Eun (1st), Cindy Pricilia Surya Prabowo (co-1st), and Sang Yup Lee (Corresponding). “Gram-per-litre scale production of lutein by engineered Corynebacterium”. Nature Synthesis (Online published)
For further information:
Sang Yup Lee, Distinguished Professor of Chemical and Biomolecular Engineering, KAIST (leesy@kaist.ac.kr, Tel: +82-42-350-3930)
2025.07.14 View 577 -
KAIST Ushers in Era of Predicting ‘Optimal Alloys’ Using AI, Without High-Temperature Experiments
<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi>
Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study draws attention as it proposes a new direction for future alloy development by predicting in advance how well alloy components will mix during melting, a long-standing challenge in the field.
KAIST (President Kwang Hyung Lee) announced on the 14th of July that Professor Seungbum Hong’s research team from the Department of Materials Science and Engineering, in international collaboration with Professor Chris Wolverton’s group at Northwestern University, has developed a high-accuracy machine learning model that predicts whether alloy components will remain stable during melting. This was achieved using formation energy data derived from Density Functional Theory (DFT)* calculations.
*Density Functional Theory (DFT): A computational quantum mechanical method used to investigate the electronic structure of many-body systems, especially atoms, molecules, and solids, based on electron density.
The research team combined formation energy values obtained via DFT with experimental melting reaction data to train a machine learning model on 4,536 binary compounds.
Among the various machine learning algorithms tested, the XGBoost-based classification model demonstrated the highest accuracy in predicting whether alloys would mix well, achieving a prediction accuracy of approximately 82.5%. The team also applied the Shapley value method* to analyze the key features of the model. One major finding was that sharp changes in the slope of the formation energy curve (referred to as “convex hull sharpness”) were the most significant factor. A steep slope indicates a composition with energetically favorable (i.e., stable) formation.
*Shapley value: An explainability method in AI used to determine how much each feature contributed to a prediction.
The most notable significance of this study is that it predicts alloy melting behavior without performing high-temperature experiments. This is especially useful for materials such as high-entropy alloys or ultra-heat-resistant alloys, which are difficult to handle experimentally. The approach could also be extended to the design of complex multi-component alloy systems in the future.
Furthermore, the physical indicators identified by the AI model showed high consistency with actual experimental results on how well alloys mix and remain stable. This suggests that the model could be broadly applied to the development of various metal materials and the prediction of structural stability.
Professor Seungbum Hong of KAIST stated, “This research demonstrates how data-driven predictive materials development is possible by integrating computational methods, experimental data, and machine learning—departing from the traditional experience-based alloy design.” He added, “In the future, by incorporating state-of-the-art AI techniques such as generative models and reinforcement learning, we could enter an era where completely new alloys are designed automatically.”
<Model performance and feature importance analysis for predicting melting congruency. (a) SHAP summary plot showing the impact of individual features on model predictions. (b) Confusion matrix illustrating the model’s classification performance. (c) Receiver operating characteristic (ROC) curve with an AUC (area under the curve) score of 0.87, indicating a strong classification performance.>
Ph.D. candidate Youngwoo Choi, from the Department of Materials Science and Engineering at KAIST, participated as the first author. The study was published in the May issue of APL Machine Learning, a prestigious journal in the field of machine learning published by the American Institute of Physics, and was selected as a “Featured Article.”
※ Paper title: Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy ※ DOI: 10.1063/5.0247514
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.14 View 176 -
Professor Jung-woo' Choi ‘s Team Comes in First at the World's Top Acoustic AI Challenge
<Photo1. (From left) Ph.D candidate Yong-hoo Kwon, M.S candidate Do-hwan Kim, Professor Jung-woo Choi, Dr. Dong-heon Lee>
'Acoustic separation and classification technology' is a next-generation artificial intelligence (AI) core technology that enables the early detection of abnormal sounds in areas such as drones, fault detection of factory pipelines, and border surveillance systems, or allows for the separation and editing of spatial audio by sound source when producing AR/VR content.
On the 11th of July, a research team led by Professor Jung-woo Choi of KAIST's Department of Electrical and Electronic Engineering won first place in the 'Spatial Semantic Segmentation of Sound Scenes' task of the 'DCASE2025 Challenge,' the world's most prestigious acoustic detection and analysis competition.
This year’s challenge featured 86 teams competing across six tasks. In this competition, the KAIST research team achieved the best performance in their first-ever participation to Task 4. Professor Jung-woo Choi’s research team consisted of Dr. Dong-heon, Lee, Ph.D. candidate Young-hoo Kwon, and M.S. candidate Do-hwan Kim.
Task 4 titled 'Spatial Semantic Segmentation of Sound Scenes' is a highly demanding task requiring the analysis of spatial information in multi-channel audio signals with overlapping sound sources. The goal was to separate individual sounds and classify them into 18 predefined categories. The research team plans to present their technology at the DCASE workshop in Barcelona this October.
<Figure 1. Example of an acoustic scene with multiple mixed sounds>
Early this year, Dr. Dong-heon Lee developed a state-of-the-art sound source separation AI that combines Transformer and Mamba architectures. During the competition, centered around researcher Young-hoo Kwon, they completed a ‘chain-of-inference architecture' AI model that performs sound source separation and classification again, using the waveforms and types of the initially separated sound sources as clues. This AI model is inspired by human’s auditory scene analysis mechanism that isolates individual sounds by focusing on incomplete clues such as sound type, rhythm, or direction, when listening to complex sounds.
Through this, the team was the only participant to achieve double-digit performance (11 dB) in 'Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi)*,' which is the measure for ranking how well the AI separated and classified sounds, proving their technical excellence.
Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi): Measures how much clearer (less distorted) the desired sound is separated and classified compared to the original audio, in dB (decibels). A higher number indicates more accurate and cleaner sound separation.
Prof. Jung-woo Choi remarked, "The research team has showcased world-leading acoustic separation AI models for the past three years, and I am delighted that these results have been officially recognized." He added, "I am proud of every member of the research team for winning first place through focused research, despite the significant increase in difficulty and having only a few weeks for development."
<Figure 2. Time-frequency patterns of sound sources separated from a mixed source>
The IEEE DCASE Challenge 2025 was held online, with submissions accepted from April 1 to June 15 and results announced on June 30. Since its launch in 2013, the DCASE Challenge has served as a premier global platform of IEEE Signal Processing Society for showcasing cutting-edge AI models in acoustic signal processing.
This research was supported by the Mid-Career Researcher Support Project and STEAM Research Project of the National Research Foundation of Korea, funded by the Ministry of Education, Science and Technology, as well as support from the Future Defense Research Center, funded by the Defense Acquisition Program Administration and the Agency for Defense Development.
2025.07.13 View 166 -
KAIST Kicks Off the Expansion of its Creative Learning Building, a 50th Anniversary Donation Landmark
KAIST announced on July 10th that it held a groundbreaking ceremony on July 9th for the expansion of its Creative Learning Building. This project, which celebrates the university's 50th anniversary, will become a significant donation-funded landmark and marks the official start of its construction.
<(From left) President Kwang Hyung Lee, Former President Sung-Chul Shin>
The groundbreaking ceremony was attended by key donors who graced the occasion, including KAIST President Kwang Hyung Lee, former President Sung-Chul Shin, Alumni Association President Yoon-Tae Lee, as well as parents and faculty member.
The Creative Learning Building serves as a primary space where KAIST undergraduate and graduate students attend lectures, functioning as a central hub for a variety of classes and talks. It also houses student support departments, including the Student Affairs Office, establishing itself as a student-centric complex that integrates educational, counseling, and welfare functions.
This expansion is more than just an increase in educational facilities; it's being developed as a "donation landmark" embodying KAIST's identity and future vision. Designed with a focus on creative convergence education, this project aims to create a new educational hub that organically combines education, exchange, and welfare functions
The campaign included over 230 participants, including KAIST alumni Byung-gyu Chang, Chairman of Krafton, former Alumni Association President Ki-chul Cha, Dr. Kun-mo Chung (former Minister of Science and Technology), as well as faculty members, parents, and current students. They collectively raised 6.5 billion KRW in donations. The total cost for this expansion project is 9 billion KRW, encompassing a gross floor area of 3,222.92㎡ across five above-ground floors, with completion targeted for September 2026.
2025.07.10 View 211 -
KAIST Shows That the Brain Can Distinguish Glucose: Clues to Treat Obesity and Diabetes
<(From left)Prof. Greg S.B Suh, Dr. Jieun Kim, Dr. Shinhye Kim, Researcher Wongyo Jeong)
“How does our brain distinguish glucose from the many nutrients absorbed in the gut?” Starting with this question, a KAIST research team has demonstrated that the brain can selectively recognize specific nutrients—particularly glucose—beyond simply detecting total calorie content. This study is expected to offer a new paradigm for appetite control and the treatment of metabolic diseases.
On the 9th, KAIST (President Kwang Hyung Lee) announced that Professor Greg S.B. Suh’s team in the Department of Biological Sciences, in collaboration with Professor Young-Gyun Park’s team (BarNeuro), Professor Seung-Hee Lee’s team (Department of Biological Sciences), and the Albert Einstein College of Medicine in New York, had identified the existence of a gut-brain circuit that allows animals in a hungry state to selectively detect and prefer glucose in the gut.
Organisms derive energy from various nutrients including sugars, proteins, and fats. Previous studies have shown that total caloric information in the gut suppresses hunger neurons in the hypothalamus to regulate appetite. However, the existence of a gut-brain circuit that specifically responds to glucose and corresponding brain cells had not been demonstrated until now.
In this study, the team successfully identified a “gut-brain circuit” that senses glucose—essential for brain function—and regulates food intake behavior for required nutrients.
They further proved, for the first time, that this circuit responds within seconds to not only hunger or external stimuli but also to specific caloric nutrients directly introduced into the small intestine, particularly D-glucose, through the activity of “CRF neurons*” in the brain’s hypothalamus.
*CRF neurons: These neurons secrete corticotropin-releasing factor (CRF) in the hypothalamus and are central to the hypothalamic-pituitary-adrenal (HPA) axis, the body’s core physiological system for responding to stress. CRF neurons are known to regulate neuroendocrine balance in response to stress stimuli.
Using optogenetics to precisely track neural activity in real time, the researchers injected various nutrients—D-glucose, L-glucose, amino acids, and fats—directly into the small intestines of mice and observed the results.
They discovered that among the CRF neurons located in the paraventricular nucleus (PVN)* of the hypothalamus, only those specific to D-glucose showed selective responses. These neurons did not respond—or showed inverse reactions—to other sugars or to proteins and fats. This is the first demonstration that single neurons in the brain can guide nutrient-specific responses depending on gut nutrient influx.
*PVN (Paraventricular Nucleus): A key nucleus within the hypothalamus responsible for maintaining bodily homeostasis.
The team also revealed that glucose-sensing signals in the small intestine are transmitted via the spinal cord to the dorsolateral parabrachial nucleus (PBNdl) of the brain, and from there to CRF neurons in the PVN. In contrast, signals for amino acids and fats are transmitted to the brain through the vagus nerve, a different pathway.
In optogenetic inhibition experiments, suppressing CRF neurons in fasting mice eliminated their preference for glucose, proving that this circuit is essential for glucose-specific nutrient preference.
This study was inspired by Professor Suh’s earlier research at NYU using fruit flies, where he identified “DH44 neurons” that selectively detect glucose and sugar in the gut. Based on the hypothesis that hypothalamic neurons in mammals would show similar functional responses to glucose, the current study was launched.
To test this hypothesis, Dr. Jineun Kim (KAIST Ph.D. graduate, now at Caltech) demonstrated during her doctoral research that hungry mice preferred glucose among various intragastrically infused nutrients and that CRF neurons exhibited rapid and specific responses.
Along with Wongyo Jung (KAIST B.S. graduate, now Ph.D. student at Caltech), they modeled and experimentally confirmed the critical role of CRF neurons. Dr. Shinhye Kim, through collaboration, revealed that specific spinal neurons play a key role in conveying intestinal nutrient information to the brain.
Dr. Jineun Kim and Dr. Shinhye Kim said, “This study started from a simple but fundamental question—‘How does the brain distinguish glucose from various nutrients absorbed in the gut?’ We have shown that spinal-based gut-brain circuits play a central role in energy metabolism and homeostasis by transmitting specific gut nutrient signals to the brain.”
Professor Suh added, “By identifying a gut-brain pathway specialized for glucose, this research offers a new therapeutic target for metabolic diseases such as obesity and diabetes. Our future research will explore similar circuits for sensing other essential nutrients like amino acids and fats and their interaction mechanisms.”
Ph.D. student Jineun Kim, Dr. Shinhye Kim, and student Wongyo Jung (co-first authors) contributed to this study, which was published online in the international journal Neuron on June 20, 2025.
※ Paper Title: Encoding the glucose identity by discrete hypothalamic neurons via the gut-brain axis ※ DOI: https://doi.org/10.1016/j.neuron.2025.05.024
This study was supported by the Samsung Science & Technology Foundation, the National Research Foundation of Korea (NRF) Leader Research Program, the POSCO Cheongam Science Fellowship, the Asan Foundation Biomedical Science Scholarship, the Institute for Basic Science (IBS), and the KAIST KAIX program.
2025.07.09 View 325