Systems+biology
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KAIST Discovers Molecular Switch that Reverses Cancerous Transformation at the Critical Moment of Transition
< (From left) PhD student Seoyoon D. Jeong, (bottom) Professor Kwang-Hyun Cho, (top) Dr. Dongkwan Shin, Dr. Jeong-Ryeol Gong >
Professor Kwang-Hyun Cho’s research team has recently been highlighted for their work on developing an original technology for cancer reversal treatment that does not kill cancer cells but only changes their characteristics to reverse them to a state similar to normal cells. This time, they have succeeded in revealing for the first time that a molecular switch that can induce cancer reversal at the moment when normal cells change into cancer cells is hidden in the genetic network.
KAIST (President Kwang-Hyung Lee) announced on the 5th of February that Professor Kwang-Hyun Cho's research team of the Department of Bio and Brain Engineering has succeeded in developing a fundamental technology to capture the critical transition phenomenon at the moment when normal cells change into cancer cells and analyze it to discover a molecular switch that can revert cancer cells back into normal cells.
A critical transition is a phenomenon in which a sudden change in state occurs at a specific point in time, like water changing into steam at 100℃. This critical transition phenomenon also occurs in the process in which normal cells change into cancer cells at a specific point in time due to the accumulation of genetic and epigenetic changes.
The research team discovered that normal cells can enter an unstable critical transition state where normal cells and cancer cells coexist just before they change into cancer cells during tumorigenesis, the production or development of tumors, and analyzed this critical transition state using a systems biology method to develop a cancer reversal molecular switch identification technology that can reverse the cancerization process. They then applied this to colon cancer cells and confirmed through molecular cell experiments that cancer cells can recover the characteristics of normal cells.
This is an original technology that automatically infers a computer model of the genetic network that controls the critical transition of cancer development from single-cell RNA sequencing data, and systematically finds molecular switches for cancer reversion by simulation analysis. It is expected that this technology will be applied to the development of reversion therapies for other cancers in the future.
Professor Kwang-Hyun Cho said, "We have discovered a molecular switch that can revert the fate of cancer cells back to a normal state by capturing the moment of critical transition right before normal cells are changed into an irreversible cancerous state."
< Figure 1. Overall conceptual framework of the technology that automatically constructs a molecular regulatory network from single-cell RNA sequencing data of colon cancer cells to discover molecular switches for cancer reversion through computer simulation analysis. Professor Kwang-Hyun Cho's research team established a fundamental technology for automatic construction of a computer model of a core gene network by analyzing the entire process of tumorigenesis of colon cells turning into cancer cells, and developed an original technology for discovering the molecular switches that can induce cancer cell reversal through attractor landscape analysis. >
He continued, "In particular, this study has revealed in detail, at the genetic network level, what changes occur within cells behind the process of cancer development, which has been considered a mystery until now." He emphasized, "This is the first study to reveal that an important clue that can revert the fate of tumorigenesis is hidden at this very critical moment of change."
< Figure 2. Identification of tumor transition state using single-cell RNA sequencing data from colorectal cancer. Using single-cell RNA sequencing data from colorectal cancer patient-derived organoids for normal and cancerous tissues, a critical transition was identified in which normal and cancerous cells coexist and instability increases (a-d). The critical transition was confirmed to show intermediate levels of major phenotypic features related to cancer or normal tissues that are indicative of the states between the normal and cancerous cells (e). >
The results of this study, conducted by KAIST Dr. Dongkwan Shin (currently at the National Cancer Center), Dr. Jeong-Ryeol Gong, and doctoral student Seoyoon D. Jeong jointly with a research team at Seoul National University that provided the organoids (in vitro cultured tissues) from colon cancer patient, were published as an online paper in the international journal ‘Advanced Science’ published by Wiley on January 22nd.
(Paper title: Attractor landscape analysis reveals a reversion switch in the transition of colorectal tumorigenesis) (DOI: https://doi.org/10.1002/advs.202412503)
< Figure 3. Reconstruction of a dynamic network model for the transition state of colorectal cancer.
A new technology was established to build a gene network computer model that can simulate the dynamic changes between genes by integrating single-cell RNA sequencing data and existing experimental results on gene-to-gene interactions in the critical transition of cancer. (a). Using this technology, a gene network computer model for the critical transition of colorectal cancer was constructed, and the distribution of attractors representing normal and cancer cell phenotypes was investigated through attractor landscape analysis (b-e). >
This study was conducted with the support of the National Research Foundation of Korea under the Ministry of Science and ICT through the Mid-Career Researcher Program and Basic Research Laboratory Program and the Disease-Centered Translational Research Project of the Korea Health Industry Development Institute (KHIDI) of the Ministry of Health and Welfare.
< Figure 4. Quantification of attractor landscapes and discovery of transcription factors for cancer reversibility through perturbation simulation analysis. A methodology for implementing discontinuous attractor landscapes continuously from a computer model of gene networks and quantifying them as cancer scores was introduced (a), and attractor landscapes for the critical transition of colorectal cancer were secured (b-d). By tracking the change patterns of normal and cancer cell attractors through perturbation simulation analysis for each gene, the optimal combination of transcription factors for cancer reversion was discovered (e-h). This was confirmed in various parameter combinations as well (i). >
< Figure 5. Identification and experimental validation of the optimal target gene for cancer reversion. Among the common target genes of the discovered transcription factor combinations, we identified cancer reversing molecular switches that are predicted to suppress cancer cell proliferation and restore the characteristics of normal colon cells (a-d). When inhibitors for the molecular switches were treated to organoids derived from colon cancer patients, it was confirmed that cancer cell proliferation was suppressed and the expression of key genes related to cancer development was inhibited (e-h), and a group of genes related to normal colon epithelium was activated and transformed into a state similar to normal colon cells (i-j). >
< Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed an original technology to systematically discover key molecular switches that can induce reversion of colon cancer cells through a systems biology approach using an attractor landscape analysis of a genetic network model for the critical transition at the moment of transformation from normal cells to cancer cells, and verified the reversing effect of actual colon cancer through cellular experiments. >
2025.02.05 View 15495 -
KAIST Develops Foundational Technology to Revert Cancer Cells to Normal Cells
Despite the development of numerous cancer treatment technologies, the common goal of current cancer therapies is to eliminate cancer cells. This approach, however, faces fundamental limitations, including cancer cells developing resistance and returning, as well as severe side effects from the destruction of healthy cells.
< (From top left) Bio and Brain Engineering PhD candidates Juhee Kim, Jeong-Ryeol Gong, Chun-Kyung Lee, and Hoon-Min Kim posed for a group photo with Professor Kwang-Hyun Cho >
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of December that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering has developed a groundbreaking technology that can treat colon cancer by converting cancer cells into a state resembling normal colon cells without killing them, thus avoiding side effects.
The research team focused on the observation that during the oncogenesis process, normal cells regress along their differentiation trajectory. Building on this insight, they developed a technology to create a digital twin of the gene network associated with the differentiation trajectory of normal cells.
< Figure 1. Technology for creating a digital twin of a gene network from single-cell transcriptome data of a normal cell differentiation trajectory. Professor Kwang-Hyun Cho's research team developed a digital twin creation technology that precisely observes the dynamics of gene regulatory relationships during the process of normal cells differentiating along a differentiation trajectory and analyzes the relationships among key genes to build a mathematical model that can be simulated (A-F). In addition, they developed a technology to discover key regulatory factors that control the differentiation trajectory of normal cells by simulating and analyzing this digital twin. >
< Figure 2. Digital twin simulation simulating the differentiation trajectory of normal colon cells. The dynamics of single-cell transcriptome data for the differentiation trajectory of normal colon cells were analyzed (A) and a digital twin of the gene network was developed representing the regulatory relationships of key genes in this differentiation trajectory (B). The simulation results of the digital twin confirm that it readily reproduces the dynamics of single-cell transcriptome data (C, D). >
Through simulation analysis, the team systematically identified master molecular switches that induce normal cell differentiation. When these switches were applied to colon cancer cells, the cancer cells reverted to a normal-like state, a result confirmed through molecular and cellular experiments as well as animal studies.
< Figure 3. Discovery of top-level key control factors that induce differentiation of normal colon cells. By applying control factor discovery technology to the digital twin model, three genes, HDAC2, FOXA2, and MYB, were discovered as key control factors that induce differentiation of normal colon cells (A, B). The results of simulation analysis of the regulatory effects of the discovered control factors through the digital twin confirmed that they could induce complete differentiation of colon cells (C). >
< Figure 4. Verification of the effect of the key control factors discovered using colon cancer cells and animal experiments on the reversibility of colon cancer. The key control factors of the normal colon cell differentiation trajectory discovered through digital twin simulation analysis were applied to actual colon cancer cells and colon cancer mouse animal models to experimentally verify the effect of cancer reversibility. The key control factors significantly reduced the proliferation of three colon cancer cell lines (A), and this was confirmed in the same way in animal models (B-D). >
This research demonstrates that cancer cell reversion can be systematically achieved by analyzing and utilizing the digital twin of the cancer cell gene network, rather than relying on serendipitous discoveries. The findings hold significant promise for developing reversible cancer therapies that can be applied to various types of cancer.
< Figure 5. The change in overall gene expression was confirmed through the regulation of the identified key regulatory factors, which converted the state of colon cancer cells to that of normal colon cells. The transcriptomes of colon cancer tissues and normal colon tissues from more than 400 colon cancer patients were compared with the transcriptomes of colon cancer cell lines and reversible colon cancer cell lines, respectively. The comparison results confirmed that the regulation of the identified key regulatory factors converted all three colon cancer cell lines to a state similar to the transcriptome expression of normal colon tissues. >
Professor Kwang-Hyun Cho remarked, "The fact that cancer cells can be converted back to normal cells is an astonishing phenomenon. This study proves that such reversion can be systematically induced."
He further emphasized, "This research introduces the novel concept of reversible cancer therapy by reverting cancer cells to normal cells. It also develops foundational technology for identifying targets for cancer reversion through the systematic analysis of normal cell differentiation trajectories."
This research included contributions from Jeong-Ryeol Gong, Chun-Kyung Lee, Hoon-Min Kim, Juhee Kim, and Jaeog Jeon, and was published in the online edition of the international journal Advanced Science by Wiley on December 11. (Title: “Control of Cellular Differentiation Trajectories for Cancer Reversion”) DOI: https://doi.org/10.1002/advs.202402132
< Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed a source technology to systematically discover key control factors that can induce reversibility of colon cancer cells through a systems biology approach and a digital twin simulation analysis of the differentiation trajectory of normal colon cells, and verified the effects of reversion on actual colon cancer through molecular cell experiments and animal experiments. >
The study was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Mid-Career Researcher Program and Basic Research Laboratory Program. The research findings have been transferred to BioRevert Inc., where they will be used for the development of practical cancer reversion therapies.
2024.12.23 View 76634 -
A KAIST Research Team Identifies a Cancer Reversion Mechanism
Despite decades of intensive cancer research by numerous biomedical scientists, cancer still holds its place as the number one cause of death in Korea. The fundamental reason behind the limitations of current cancer treatment methods is the fact that they all aim to completely destroy cancer cells, which eventually allows the cancer cells to acquire immunity. In other words, recurrences and side-effects caused by the destruction of healthy cells are inevitable. To this end, some have suggested anticancer treatment methods based on cancer reversion, which can revert cancer cells back to normal or near-normal cells under certain conditions. However, the practical development of this idea has not yet been attempted.
On June 8, a KAIST research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering reported to have successfully identified the fundamental principle of a process that can revert cancer cells back to normal cells without killing the cells.
Professor Cho’s team focused on the fact that unlike normal cells, which react according to external stimuli, cancer cells tend to ignore such stimuli and only undergo uncontrolled cell division. Through computer simulation analysis, the team discovered that the input-output (I/O) relationships that were distorted by genetic mutations could be reverted back to normal I/O relationships under certain conditions. The team then demonstrated through molecular cell experiments that such I/O relationship recovery also occurred in real cancer cells.
The results of this study, written by Dr. Jae Il Joo and Dr. Hwa-Jeong Park, were published in Wiley’s Advanced Science online on June 2 under the title, "Normalizing input-output relationships of cancer networks for reversion therapy."
< Image 1. Input-output (I/O) relationships in gene regulatory networks >
Professor Kwang-Hyun Cho's research team classified genes into four types by simulation-analyzing the effect of gene mutations on the I/O relationship of gene regulatory networks. (Figure A-J) In addition, by analyzing 18 genes of the cancer-related gene regulatory network, it was confirmed that when mutations occur in more than half of the genes constituting each network, reversibility is possible through appropriate control. (Figure K)
Professor Cho’s team uncovered that the reason the distorted I/O relationships of cancer cells could be reverted back to normal ones was the robustness and redundancy of intracellular gene control networks that developed over the course of evolution. In addition, they found that some genes were more promising as targets for cancer reversion than others, and showed through molecular cell experiments that controlling such genes could revert the distorted I/O relationships of cancer cells back to normal ones.
< Image 2. Simulation results of restoration of bladder cancer gene regulation network and I/O relationship of bladder cancer cells. >
The research team classified the effects of gene mutations on the I/O relationship in the bladder cancer gene regulation network by simulation analysis and classified them into 4 types. (Figure A) Through this, it was found that the distorted input-output relationship between bladder cancer cell lines KU-1919 and HCT-1197 could be restored to normal. (Figure B)
< Image 3. Analysis of survival of bladder cancer patients according to reversible gene mutation and I/O recovery experiment of bladder cancer cells. >
As predicted through network simulation analysis, Professor Kwang-Hyun Cho's research team confirmed through molecular cell experiments that the response to TGF-b was normally restored when AKT and MAP3K1 were inhibited in the bladder cancer cell line KU-1919. (Figure A-G) In addition, it was confirmed that there is a difference in the survival rate of bladder cancer patients depending on the presence or absence of a reversible gene mutation. (Figure H)
The results of this research show that the reversion of real cancer cells does not happen by chance, and that it is possible to systematically explore targets that can induce this phenomenon, thereby creating the potential for the development of innovative anticancer drugs that can control such target genes.
< Image 4. Cancer cell reversibility principle >
The research team analyzed the reversibility, redundancy, and robustness of various networks and found that there was a positive correlation between them. From this, it was found that reversibility was additionally inherent in the process of evolution in which the gene regulatory network acquired redundancy and consistency.
Professor Cho said, “By uncovering the fundamental principles of a new cancer reversion treatment strategy that may overcome the unresolved limitations of existing chemotherapy, we have increased the possibility of developing new and innovative drugs that can improve both the prognosis and quality of life of cancer patients.”
< Image 5. Conceptual diagram of research results >
The research team identified the fundamental control principle of cancer cell reversibility through systems biology research. When the I/O relationship of the intracellular gene regulatory network is distorted by mutation, the distorted I/O relationship can be restored to a normal state by identifying and adjusting the reversible gene target based on the redundancy of the molecular circuit inherent in the complex network.
After Professor Cho’s team first suggested the concept of reversion treatment, they published their results for reverting colorectal cancer in January 2020, and in January 2022 they successfully re-programmed malignant breast cancer cells back into hormone-treatable ones. In January 2023, the team successfully removed the metastasis ability from lung cancer cells and reverted them back to a state that allowed improved drug reactivity. However, these results were case studies of specific types of cancer and did not reveal what common principle allowed cancer reversion across all cancer types, making this the first revelation of the general principle of cancer reversion and its evolutionary origins.
This research was funded by the Ministry of Science and ICT of the Republic of Korea and the National Research Foundation of Korea.
2023.06.20 View 8670 -
Connecting the Dots to Find New Treatments for Breast Cancer
Systems biologists uncovered new ways of cancer cell reprogramming to treat drug-resistant cancers
Scientists at KAIST believe they may have found a way to reverse an aggressive, treatment-resistant type of breast cancer into a less dangerous kind that responds well to treatment. The study involved the use of mathematical models to untangle the complex genetic and molecular interactions that occur in the two types of breast cancer, but could be extended to find ways for treating many others. The study’s findings were published in the journal Cancer Research.
Basal-like tumours are the most aggressive type of breast cancer, with the worst prognosis. Chemotherapy is the only available treatment option, but patients experience high recurrence rates. On the other hand, luminal-A breast cancer responds well to drugs that specifically target a receptor on their cell surfaces, called estrogen receptor alpha (ERα).
KAIST systems biologist Kwang-Hyun Cho and colleagues analyzed the complex molecular and genetic interactions of basal-like and luminal-A breast cancers to find out if there might be a way to switch the former to the latter and give patients a better chance to respond to treatment.
To do this, they accessed large amounts of cancer and patient data to understand which genes and molecules are involved in the two types. They then input this data into a mathematical model that represents genes, proteins and molecules as dots and the interactions between them as lines. The model can be used to conduct simulations and see how interactions change when certain genes are turned on or off.
“There have been a tremendous number of studies trying to find therapeutic targets for treating basal-like breast cancer patients,” says Cho. “But clinical trials have failed due to the complex and dynamic nature of cancer. To overcome this issue, we looked at breast cancer cells as a complex network system and implemented a systems biological approach to unravel the underlying mechanisms that would allow us to reprogram basal-like into luminal-A breast cancer cells.”
Using this approach, followed by experimental validation on real breast cancer cells, the team found that turning off two key gene regulators, called BCL11A and HDAC1/2, switched a basal-like cancer signalling pathway into a different one used by luminal-A cancer cells. The switch reprograms the cancer cells and makes them more responsive to drugs that target ERα receptors. However, further tests will be needed to confirm that this also works in animal models and eventually humans.
“Our study demonstrates that the systems biological approach can be useful for identifying novel therapeutic targets,” says Cho.
The researchers are now expanding its breast cancer network model to include all breast cancer subtypes. Their ultimate aim is to identify more drug targets and to understand the mechanisms that could drive drug-resistant cells to turn into drug-sensitive ones.
This work was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, Electronics and Telecommunications Research Institute, and the KAIST Grand Challenge 30 Project.
-Publication Sea R. Choi, Chae Young Hwang, Jonghoon Lee, and Kwang-Hyun Cho, “Network Analysis Identifies Regulators of Basal-like Breast Cancer Reprogramming and Endocrine TherapyVulnerability,” Cancer Research, November 30. (doi:10.1158/0008-5472.CAN-21-0621)
-ProfileProfessor Kwang-Hyun ChoLaboratory for Systems Biology and Bio-Inspired EngineeringDepartment of Bio and Brain EngineeringKAIST
2021.12.07 View 9554 -
3D Visualization and Quantification of Bioplastic PHA in a Living Bacterial Cell
3D holographic microscopy leads to in-depth analysis of bacterial cells accumulating the bacterial bioplastic, polyhydroxyalkanoate (PHA)
A research team at KAIST has observed how bioplastic granule is being accumulated in living bacteria cells through 3D holographic microscopy. Their 3D imaging and quantitative analysis of the bioplastic ‘polyhydroxyalkanoate’ (PHA) via optical diffraction tomography provides insights into biosynthesizing sustainable substitutes for petroleum-based plastics.
The bio-degradable polyester polyhydroxyalkanoate (PHA) is being touted as an eco-friendly bioplastic to replace existing synthetic plastics. While carrying similar properties to general-purpose plastics such as polyethylene and polypropylene, PHA can be used in various industrial applications such as container packaging and disposable products.
PHA is synthesized by numerous bacteria as an energy and carbon storage material under unbalanced growth conditions in the presence of excess carbon sources. PHA exists in the form of insoluble granules in the cytoplasm. Previous studies on investigating in vivo PHA granules have been performed by using fluorescence microscopy, transmission electron microscopy (TEM), and electron cryotomography.
These techniques have generally relied on the statistical analysis of multiple 2D snapshots of fixed cells or the short-time monitoring of the cells. For the TEM analysis, cells need to be fixed and sectioned, and thus the investigation of living cells was not possible. Fluorescence-based techniques require fluorescence labeling or dye staining. Thus, indirect imaging with the use of reporter proteins cannot show the native state of PHAs or cells, and invasive exogenous dyes can affect the physiology and viability of the cells. Therefore, it was difficult to fully understand the formation of PHA granules in cells due to the technical limitations, and thus several mechanism models based on the observations have been only proposed.
The team of metabolic engineering researchers led by Distinguished Professor Sang Yup Lee and Physics Professor YongKeun Park, who established the startup Tomocube with his 3D holographic microscopy, reported the results of 3D quantitative label-free analysis of PHA granules in individual live bacterial cells by measuring the refractive index distributions using optical diffraction tomography. The formation and growth of PHA granules in the cells of Cupriavidus necator, the most-studied native PHA (specifically, poly(3-hydroxybutyrate), also known as PHB) producer, and recombinant Escherichia coli harboring C. necator PHB biosynthesis pathway were comparatively examined.
From the reconstructed 3D refractive index distribution of the cells, the team succeeded in the 3D visualization and quantitative analysis of cells and intracellular PHA granules at a single-cell level. In particular, the team newly presented the concept of “in vivo PHA granule density.” Through the statistical analysis of hundreds of single cells accumulating PHA granules, the distinctive differences of density and localization of PHA granules in the two micro-organisms were found. Furthermore, the team identified the key protein that plays a major role in making the difference that enabled the characteristics of PHA granules in the recombinant E. coli to become similar to those of C. necator.
The research team also presented 3D time-lapse movies showing the actual processes of PHA granule formation combined with cell growth and division. Movies showing the living cells synthesizing and accumulating PHA granules in their native state had never been reported before.
Professor Lee said, “This study provides insights into the morphological and physical characteristics of in vivo PHA as well as the unique mechanisms of PHA granule formation that undergo the phase transition from soluble monomers into the insoluble polymer, followed by granule formation. Through this study, a deeper understanding of PHA granule formation within the bacterial cells is now possible, which has great significance in that a convergence study of biology and physics was achieved. This study will help develop various bioplastics production processes in the future.”
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (Grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) and the Bio & Medical Technology Development Program (Grant No. 2021M3A9I4022740) from the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea to S.Y.L. This work was also supported by the KAIST Cross-Generation Collaborative Laboratory project.
-PublicationSo Young Choi, Jeonghun Oh, JaeHwang Jung, YongKeun Park, and Sang Yup Lee. Three-dimensional label-free visualization and quantification of polyhydroxyalkanoates in individualbacterial cell in its native state. PNAS(https://doi.org./10.1073/pnas.2103956118)
-ProfileDistinguished Professor Sang Yup LeeMetabolic Engineering and Synthetic Biologyhttp://mbel.kaist.ac.kr/
Department of Chemical and Biomolecular Engineering KAIST
Endowed Chair Professor YongKeun ParkBiomedical Optics Laboratoryhttps://bmokaist.wordpress.com/
Department of PhysicsKAIST
2021.07.28 View 12317 -
E. coli Engineered to Grow on CO₂ and Formic Acid as Sole Carbon Sources
- An E. coli strain that can grow to a relatively high cell density solely on CO₂ and formic acid was developed by employing metabolic engineering. -
Most biorefinery processes have relied on the use of biomass as a raw material for the production of chemicals and materials. Even though the use of CO₂ as a carbon source in biorefineries is desirable, it has not been possible to make common microbial strains such as E. coli grow on CO₂.
Now, a metabolic engineering research group at KAIST has developed a strategy to grow an E. coli strain to higher cell density solely on CO₂ and formic acid. Formic acid is a one carbon carboxylic acid, and can be easily produced from CO₂ using a variety of methods. Since it is easier to store and transport than CO₂, formic acid can be considered a good liquid-form alternative of CO₂.
With support from the C1 Gas Refinery R&D Center and the Ministry of Science and ICT, a research team led by Distinguished Professor Sang Yup Lee stepped up their work to develop an engineered E. coli strain capable of growing up to 11-fold higher cell density than those previously reported, using CO₂ and formic acid as sole carbon sources. This work was published in Nature Microbiology on September 28.
Despite the recent reports by several research groups on the development of E. coli strains capable of growing on CO₂ and formic acid, the maximum cell growth remained too low (optical density of around 1) and thus the production of chemicals from CO₂ and formic acid has been far from realized.
The team previously reported the reconstruction of the tetrahydrofolate cycle and reverse glycine cleavage pathway to construct an engineered E. coli strain that can sustain growth on CO₂ and formic acid. To further enhance the growth, the research team introduced the previously designed synthetic CO₂ and formic acid assimilation pathway, and two formate dehydrogenases.
Metabolic fluxes were also fine-tuned, the gluconeogenic flux enhanced, and the levels of cytochrome bo3 and bd-I ubiquinol oxidase for ATP generation were optimized. This engineered E. coli strain was able to grow to a relatively high OD600 of 7~11, showing promise as a platform strain growing solely on CO₂ and formic acid.
Professor Lee said, “We engineered E. coli that can grow to a higher cell density only using CO₂ and formic acid. We think that this is an important step forward, but this is not the end. The engineered strain we developed still needs further engineering so that it can grow faster to a much higher density.”
Professor Lee’s team is continuing to develop such a strain. “In the future, we would be delighted to see the production of chemicals from an engineered E. coli strain using CO₂ and formic acid as sole carbon sources,” he added.
-Profile:Distinguished Professor Sang Yup Leehttp://mbel.kaist.ac.krDepartment of Chemical and Biomolecular EngineeringKAIST
2020.09.29 View 10952 -
Deep Learning-Powered 'DeepEC' Helps Accurately Understand Enzyme Functions
(Figure: Overall scheme of DeepEC)
A deep learning-powered computational framework, ‘DeepEC,’ will allow the high-quality and high-throughput prediction of enzyme commission numbers, which is essential for the accurate understanding of enzyme functions.
A team of Dr. Jae Yong Ryu, Professor Hyun Uk Kim, and Distinguished Professor Sang Yup Lee at KAIST reported the computational framework powered by deep learning that predicts enzyme commission (EC) numbers with high precision in a high-throughput manner.
DeepEC takes a protein sequence as an input and accurately predicts EC numbers as an output. Enzymes are proteins that catalyze biochemical reactions and EC numbers consisting of four level numbers (i.e., a.b.c.d) indicate biochemical reactions. Thus, the identification of EC numbers is critical for accurately understanding enzyme functions and metabolism.
EC numbers are usually given to a protein sequence encoding an enzyme during a genome annotation procedure. Because of the importance of EC numbers, several EC number prediction tools have been developed, but they have room for further improvement with respect to computation time, precision, coverage, and the total size of the files needed for the EC number prediction.
DeepEC uses three convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers if the three CNNs do not produce reliable EC numbers for a given protein sequence. DeepEC was developed by using a gold standard dataset covering 1,388,606 protein sequences and 4,669 EC numbers.
In particular, benchmarking studies of DeepEC and five other representative EC number prediction tools showed that DeepEC made the most precise and fastest predictions for EC numbers. DeepEC also required the smallest disk space for implementation, which makes it an ideal third-party software component.
Furthermore, DeepEC was the most sensitive in detecting enzymatic function loss as a result of mutations in domains/binding site residue of protein sequences; in this comparative analysis, all the domains or binding site residue were substituted with L-alanine residue in order to remove the protein function, which is known as the L-alanine scanning method.
This study was published online in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on June 20, 2019, entitled “Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.”
“DeepEC can be used as an independent tool and also as a third-party software component in combination with other computational platforms that examine metabolic reactions. DeepEC is freely available online,” said Professor Kim.
Distinguished Professor Lee said, “With DeepEC, it has become possible to process ever-increasing volumes of protein sequence data more efficiently and more accurately.”
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation of Korea. This work was also funded by the Bio & Medical Technology Development Program of the National Research Foundation of Korea funded by the Korean government, the Ministry of Science and ICT.
Profile:
-Professor Hyun Uk Kim (ehukim@kaist.ac.kr)
https://sites.google.com/view/ehukim
Department of Chemical and Biomolecular Engineering
-Distinguished Professor Sang Yup Lee (leesy@kaist.ac.kr)
Department of Chemical and Biomolecular Engineering
http://mbel.kaist.ac.kr
2019.07.09 View 36706 -
Three Professors Named KAST Fellows
(Professor Dan Keun Sung at the center)
(Professor Y.H. Cho at the center)
(Professor K.H. Cho at the center)
The Korean Academy of Science and Technology (KAST) inducted three KAIST professors as fellows at the New Year’s ceremony held at KAST on January 12. They were among the 24 newly elected fellows of the most distinguished academy in Korea. The new fellows are Professor Dan Keun Sung of the School of Electrical Engineering, Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering, and Professor Yong-Hoon Cho of the Department of Physics.
Professor Sung was recognized for his lifetime academic achievements in fields related with network protocols and energy ICT. He also played a crucial role in launching the Korean satellites KITSAT-1,2,3 and the establishment of the Satellite Technology Research Center at KAIST.
Professor Y.H.Cho has been a pioneer in the field of low-dimensional semiconductor-powered quantum photonics that enables quantum optical research in solid state. He has been recognized as a renowned scholar in this field internationally.
Professor K.H.Cho has conducted original research that combines IT and BT in systems biology and has applied novel technologies of electronic modeling and computer simulation analysis for investigating complex life sciences. Professor Cho, who is in his 40s, is the youngest fellow among the newly inducted fellows.
2018.01.16 View 13699 -
Professor Kwang-Hyun Cho Recognzied by "Scientist of the Month" Award
Professor Kwang-Hyun Cho of KAIST’s Department of Bio and Brain Engineering received the “Scientist of the Month” award in February 2015 from the Ministry of Science, ICT, and Future Planning of the Republic of Korea and the National Research Foundation of Korea. The award was in recognition of Professor Cho’s contribution to the advanced technique of controlling the death of cancer cells based on systems biology, a convergence research in information technology (IT) and biotechnology.
Professor Cho has published around 140 articles in international journals, including 34 papers in renowned science journals such as Nature, Science, and Cell in the past three years. His work also includes systems biology textbooks and many entries in international academic encyclopaedia.
His field, systems biology, is a new biological research paradigm that identifies and controls the fundamental principles of organisms on a systems level.
A well-known tumour suppressor protein, p53, is known to suppress abnormal cell growth and promote apoptosis of can cells, and thus was a focus of research by many scientists, but its effect has been insignificant and brought many side effects. This was due to the complex function of p53 that controls various positive and negative feedbacks. Therefore, there was a limit to understanding the protein with the existing biological approach.
However, Professor Cho found the kinetic change and function of p53 via a systems biology approach. By applying IT technology to complex biological networks, he also identified the response to stress and the survival and death signal transduction pathways of cardiomyocytes and developed new control methods for cancer cells.
Professor Cho said, “This award served as a momentum to turn over a new leaf.” He added, “I hope convergence research such as my field will bring more innovative ideas on the boundaries of academia.”
2015.02.09 View 12802 -
Professor Kwang-Hyun Cho publishes Encyclopaedia of Systems Biology
Professor Kwang-Hyun Cho
KAIST Biological and Brain Engineering Department’s Professor Kwang-Hyun Cho edited the Encyclopaedia of Systems Biology with three scholars, all experts of Systems Biology in England, Germany and the United States. It is rare that a Korean scientist edits a world renowned academic science encyclopaedia.
The Encyclopaedia, published by the New York office of Springer Verlag, was a grand international project five years in the making by 28 editors and 391 scientists with expertise in Systems Biology from around the world. The Encyclopaedia compiles various research areas of Systems Biology, the new academic paradigm of the 21st century through the integration of IT and BT, comprehensively on 3,000 pages in 4 four volumes.
Professor Kwang-Hyun Cho, who led this international project, majored in electrical engineering and pioneered the field of Systems Biology, the integrated study of biological sciences and engineering, as a new integrated field of IT since the 1990s. The professor has achieved various innovative research results since then. Recently he has investigated “kernel,” an evolutionary core structure in complex biological networks and developed a new cancer treatment through the state space analysis of the molecular network of cancer cells. His work was published in Science Signalling, a sister journal of Science, as a cover story several times, and contributed to foundational research as well as commercialisation of the integrated fields of IT and BT.
2013.08.27 View 9716 -
Future of Petrochemical Industry: The Age of Bio-Refineries
The concept of bio-refinery is based on using biomass from seaweeds and non-edible plant sources to produce various materials.
Bio-refineries has been looked into with increasing interest in modern times due to the advent of global warming (and the subsequent changes in the atmosphere) and the exhaustion of natural resources.
However past 20 years of research in metabolic engineering had a crucial limitation; the need to improve the efficiency of the microorganisms that actually go about converting biomass into biochemical materials.
In order to compensate for the inefficiency, Professor Lee Sang Yeop combined systems biology, composite biology, evolutionary engineering to form ‘systems metabolic engineering’.
This allows combining various data to explain the organism’s state in a multi-dimensional scope and respond accordingly by controlling the metabolism.
The result of the experiment is set as the cover dissertation of ‘Trends in Biotechnology’ magazine’s August edition.
2011.07.28 View 11746 -
New drug targeting method for microbial pathogens developed using in silico cell
A ripple effect is expected on the new antibacterial discovery using “in silico” cells
Featured as a journal cover paper of Molecular BioSystems
A research team of Distinguished Professor Sang Yup Lee at KAIST recently constructed an in silico cell of a microbial pathogen that is resistant to antibiotics and developed a new drug targeting method that could effectively disrupt the pathogen"s growth using the in silico cell.
Hyun Uk Kim, a graduate research assistant at the Department of Chemical and Biomolecular Engineering, KAIST, conducted this study as a part of his thesis research, and the study was featured as a journal cover paper in the February issue of Molecular BioSystems this year, published by The Royal Society of Chemistry based in Europe.
It was relatively easy to treat infectious microbes using antibiotics in the past. However, the overdose of antibiotics has caused pathogens to increase their resistance to various antibiotics, and it has become more difficult to cure infectious diseases these days.
A representative microbial pathogen is Acinetobacter baumannaii. Originally isolated from soils and water, this microorganism did not have resistance to antibiotics, and hence it was easy to eradicate them if infected. However, within a decade, this miroorganism has transformed into a dreadful super-bacterium resistant to antibiotics and caused many casualties among the U.S. and French soldiers who were injured from the recent Iraqi war and infected with Acinetobacter baumannaii.
Professor Lee’s group constructed an in silico cell of this A. baumannii by computationally collecting, integrating, and analyzing the biological information of the bacterium, scattered over various databases and literatures, in order to study this organism"s genomic features and system-wide metabolic characteristics. Furthermore, they employed this in silico cell for integrative approaches, including several network analysis and analysis of essential reactions and metabolites, to predict drug targets that effectively disrupt the pathogen"s growth. Final drug targets are the ones that selectively kill pathogens without harming human body.
Here, essential reactions refer to enzymatic reactions required for normal metabolic functioning in organisms, while essential metabolites indicate chemical compounds required in the metabolism for proper functioning, and their removal brings about the effect of simultaneously disrupting their associated enzymes that interact with them.
This study attempted to predict highly reliable drug targets by systematically scanning biological components, including metabolic genes, enzymatic reactions, that constitute an in silico cell in a short period of time.
This research achievement is highly regarded as it, for the first time, systematically scanned essential metabolites for the effective drug targets using the concept of systems biology, and paved the way for a new antibacterial discovery. This study is also expected to contribute to elucidating the infectious mechanism caused by pathogens.
"Although tons of genomic information is poured in at this moment, application research that efficiently converts this preliminary information into actually useful information is still lagged behind. In this regard, this study is meaningful in that medically useful information is generated from the genomic information of Acinetobacter baumannii," says Professor Lee. "In particular, development of this organism"s in silico cell allows generation of new knowledge regarding essential genes and enzymatic reactions under specific conditions," he added.
This study was supported by the Korean Systems Biology Project of the Ministry of Education, Science and Technology, and the patent for the development of in silico cells of microbial pathogens and drug targeting methods has been filed.
[Picture 1 Cells in silico]
[Picture 2 A process of generating drug targets without harming human body while effectively disrupting the growth of a pathogen, after predicting metabolites from in silico cells]
2010.04.05 View 16680