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Using light to throw and catch atoms to open up a new chapter for quantum computing
The technology to move and arrange atoms, the most basic component of a quantum computer, is very important to Rydberg quantum computing research. However, to place the atoms at the desired location, the atoms must be captured and transported one by one using a highly focused laser beam, commonly referred to as an optical tweezer. and, the quantum information of the atoms is likely to change midway.
KAIST (President Kwang Hyung Lee) announced on the 27th that a research team led by Professor Jaewook Ahn of the Department of Physics developed a technology to throw and receive rubidium atoms one by one using a laser beam.
The research team developed a method to throw and receive atoms which would minimize the time the optical tweezers are in contact with the atoms in which the quantum information the atoms carry may change. The research team used the characteristic that the rubidium atoms, which are kept at a very low temperature of 40μK below absolute zero, move very sensitively to the electromagnetic force applied by light along the focal point of the light tweezers.
The research team accelerated the laser of an optical tweezer to give an optical kick to an atom to send it to a target, then caught the flying atom with another optical tweezer to stop it. The atom flew at a speed of 65 cm/s, and traveled up to 4.2 μm. Compared to the existing technique of guiding the atoms with the optical tweezers, the technique of throwing and receiving atoms eliminates the need to calculate the transporting path for the tweezers, and makes it easier to fix the defects in the atomic arrangement. As a result, it is effective in generating and maintaining a large number of atomic arrangements, and when the technology is used to throw and receive flying atom qubits, it will be used in studying new and more powerful quantum computing methods that presupposes the structural changes in quantum arrangements.
"This technology will be used to develop larger and more powerful Rydberg quantum computers," says Professor Jaewook Ahn. “In a Rydberg quantum computer,” he continues, “atoms are arranged to store quantum information and interact with neighboring atoms through electromagnetic forces to perform quantum computing. The method of throwing an atom away for quick reconstruction the quantum array can be an effective way to fix an error in a quantum computer that requires a removal or replacement of an atom.”
The research, which was conducted by doctoral students Hansub Hwang and Andrew Byun of the Department of Physics at KAIST and Sylvain de Léséleuc, a researcher at the National Institute of Natural Sciences in Japan, was published in the international journal, Optica, 0n March 9th. (Paper title: Optical tweezers throw and catch single atoms).
This research was carried out with the support of the Samsung Science & Technology Foundation.
<Figure 1> A schematic diagram of the atom catching and throwing technique. The optical tweezer on the left kicks the atom to throw it into a trajectory to have the tweezer on the right catch it to stop it.
2023.03.28 View 4218 -
KAIST research team develops clathrin assembly for targeted protein delivery to cancer cells
In order to effectively treat cancer without additional side effects, we need a way to deliver drugs specifically to tumor cells. Protein assemblies have been widely used for drug delivery in the field of cancer treatment, but to use them for drug delivery they must first be functionalized, meaning they must be bound to the protein that recognizes the target tumor cell and deliver a drug that kills it. However, the functionalization process of protein assemblies is very complex, inefficient, and limited to small-sized chemical drugs, which limits their real-life applicability.
On March 14, a KAIST research team led by Professor Hak-Sung Kim from the KAIST Department of Biological Sciences reported the development of a clathrin assembly that can specifically deliver drugs to cancer cells.
Clathrin assemblies transport materials efficiently through endocytosis in living organisms. They are formed by the self-assembly of triskelion units, which are composed of three heavy chains bonded with three light chains. Inspired by this mechanism, the research team designed a clathrin chain to facilitate the functionalization of tumor cell recognition proteins and toxin proteins in order to deliver drugs specifically to tumor cells. From this, the team created a new type of clathrin assembly.
Figure 1. (Upper) Schematic diagram of the development of a new clathrin assembly that simultaneously functionalizes two types of proteins (cancer cell recognition protein and toxin protein) on heavy and light chains of clathrin in a one-pot reaction (bottom, left) Electron microscopy image of clathrin assembly: formation of an assembly with a diameter of about 28 nanometers (bottom, right) Cancer cell killing effect of CLA: CLA functionalized with epidermal growth factor receptor (EGFR) recognition protein and toxin protein kills only the cancer cells that overexpress EGFR.
The newly developed clathrin assembly requires a one-pot reaction, meaning both the toxin and tumor-recognition proteins can be functionalized simultaneously and show high efficiency. As a result, this technique is expected to be used in a wide variety of applications in the fields of biology and medicine including drug delivery, vaccine development, and diagnosing illnesses.
In this research, an epidermal growth factor receptor (EGFR), a common tumor marker, was used as the recognition protein, allowing drug delivery only to tumor cells. The clathrin assemblies that were functionalized to recognize EGFR showed a bonding strength 900-times stronger than it normally would due to the avidity effect. Based on this finding, the research team confirmed that treatment with toxin-functionalized clathrin assembly led to effective cell death for tumor cells, while it showed no such effect on healthy cells.
This research by Dr. Hong-Sik Kim and his colleagues was published in Small volume 19, issue 8 on February 22 under the title, "Construction and Functionalization of a Clathrin Assembly for a Targeted Protein Delivery", and it was selected as the cover paper.
Figure 2. Cover Paper: This study was published in the international journal 'Small' on February 22nd, Volume 19, No. 8, and was selected as the cover paper.
First author Dr. Hong-Sik Kim said, “Clathrin is difficult to functionalize, and since it is extracted from mammals, realistic applications have been limited.” He added, “But the new clathrin assembly we designed for this research can be functionalized with two different types of proteins through a single-step reaction, and can be produced from E. coli, meaning it can become an applicable protein assembly technology for a wide range of biomedical fields.”
This research was funded by the Global Ph.D. Fellowship and the Mid-career Researcher Grant of the National Research Foundation.
2023.03.22 View 4075 -
KAIST leads AI-based analysis on drug-drug interactions involving Paxlovid
KAIST (President Kwang Hyung Lee) announced on the 16th that an advanced AI-based drug interaction prediction technology developed by the Distinguished Professor Sang Yup Lee's research team in the Department of Biochemical Engineering that analyzed the interaction between the PaxlovidTM ingredients that are used as COVID-19 treatment and other prescription drugs was published as a thesis. This paper was published in the online edition of 「Proceedings of the National Academy of Sciences of America」 (PNAS), an internationally renowned academic journal, on the 13th of March.
* Thesis Title: Computational prediction of interactions between Paxlovid and prescription drugs (Authored by Yeji Kim (KAIST, co-first author), Jae Yong Ryu (Duksung Women's University, co-first author), Hyun Uk Kim (KAIST, co-first author), and Sang Yup Lee (KAIST, corresponding author))
In this study, the research team developed DeepDDI2, an advanced version of DeepDDI, an AI-based drug interaction prediction model they developed in 2018. DeepDDI2 is able to compute for and process a total of 113 drug-drug interaction (DDI) types, more than the 86 DDI types covered by the existing DeepDDI.
The research team used DeepDDI2 to predict possible interactions between the ingredients (ritonavir, nirmatrelvir) of Paxlovid*, a COVID-19 treatment, and other prescription drugs. The research team said that while among COVID-19 patients, high-risk patients with chronic diseases such as high blood pressure and diabetes are likely to be taking other drugs, drug-drug interactions and adverse drug reactions for Paxlovid have not been sufficiently analyzed, yet. This study was pursued in light of seeing how continued usage of the drug may lead to serious and unwanted complications.
* Paxlovid: Paxlovid is a COVID-19 treatment developed by Pfizer, an American pharmaceutical company, and received emergency use approval (EUA) from the US Food and Drug Administration (FDA) in December 2021.
The research team used DeepDDI2 to predict how Paxrovid's components, ritonavir and nirmatrelvir, would interact with 2,248 prescription drugs. As a result of the prediction, ritonavir was predicted to interact with 1,403 prescription drugs and nirmatrelvir with 673 drugs.
Using the prediction results, the research team proposed alternative drugs with the same mechanism but low drug interaction potential for prescription drugs with high adverse drug events (ADEs). Accordingly, 124 alternative drugs that could reduce the possible adverse DDI with ritonavir and 239 alternative drugs for nirmatrelvir were identified.
Through this research achievement, it became possible to use an deep learning technology to accurately predict drug-drug interactions (DDIs), and this is expected to play an important role in the digital healthcare, precision medicine and pharmaceutical industries by providing useful information in the process of developing new drugs and making prescriptions.
Distinguished Professor Sang Yup Lee said, "The results of this study are meaningful at times like when we would have to resort to using drugs that are developed in a hurry in the face of an urgent situations like the COVID-19 pandemic, that it is now possible to identify and take necessary actions against adverse drug reactions caused by drug-drug interactions very quickly.”
This research was carried out with the support of the KAIST New-Deal Project for COVID-19 Science and Technology and the Bio·Medical Technology Development Project supported by the Ministry of Science and ICT.
Figure 1. Results of drug interaction prediction between Paxlovid ingredients and representative approved drugs using DeepDDI2
2023.03.16 View 5198 -
The cause of disability in aged brain meningeal membranes identified
Due to the increase in average age, studies on changes in the brain following general aging process without serious brain diseases have also become an issue that requires in-depth studies. Regarding aging research, as aging progresses, ‘sugar’ accumulates in the body, and the accumulated sugar becomes a causative agent for various diseases such as aging-related inflammation and vascular disease. In the end, “surplus” sugar molecules attach to various proteins in the body and interfere with their functions.
KAIST (President Kwang Hyung Lee), a joint research team of Professor Pilnam Kim and Professor Yong Jeong of the Department of Bio and Brain Engineering, revealed on the 15th that it was confirmed that the function of being the “front line of defense” for the cerebrocortex of the brain meninges, the layers of membranes that surrounds the brain, is hindered when 'sugar' begins to build up on them as aging progresses.
Professor Kim's research team confirmed excessive accumulation of sugar molecules in the meninges of the elderly and also confirmed that sugar accumulation occurs mouse models in accordance with certain age levels. The meninges are thin membranes that surround the brain and exist at the boundary between the cerebrospinal fluid and the cortex and play an important role in protecting the brain. In this study, it was revealed that the dysfunction of these brain membranes caused by aging is induced by 'excess' sugar in the brain. In particular, as the meningeal membrane becomes thinner and stickier due to aging, a new paradigm has been provided for the discovery of the principle of the decrease in material exchange between the cerebrospinal fluid and the cerebral cortex.
This research was conducted by the Ph.D. candidate Hyo Min Kim and Dr. Shinheun Kim as the co-first authors to be published online on February 28th in the international journal, Aging Cell. (Paper Title: Glycation-mediated tissue-level remodeling of brain meningeal membrane by aging)
The meninges, which are in direct contact with the cerebrospinal fluid, are mainly composed of collagen, an extracellular matrix (ECM) protein, and are composed of fibroblasts, which are cells that produce this protein. The cells that come in contact with collagen proteins that are attached with sugar have a low collagen production function, while the meningeal membrane continuously thins and collapses as the expression of collagen degrading enzymes increases.
Studies on the relationship between excess sugar molecules accumulation in the brain due to continued sugar intake and the degeneration of neurons and brain diseases have been continuously conducted. However, this study was the first to identify meningeal degeneration and dysfunction caused by glucose accumulation with the focus on the meninges itself, and the results are expected to present new ideas for research into approach towards discoveries of new treatments for brain disease.
Researcher Hyomin Kim, the first author, introduced the research results as “an interesting study that identified changes in the barriers of the brain due to aging through a convergent approach, starting from the human brain and utilizing an animal model with a biomimetic meningeal model”.
Professor Pilnam Kim's research team is conducting research and development to remove sugar that accumulated throughout the human body, including the meninges. Advanced glycation end products, which are waste products formed when proteins and sugars meet in the human body, are partially removed by macrophages. However, glycated products bound to extracellular matrix proteins such as collagen are difficult to remove naturally. Through the KAIST-Ceragem Research Center, this research team is developing a healthcare medical device to remove 'sugar residue' in the body.
This study was carried out with the National Research Foundation of Korea's collective research support.
Figure 1. Schematic diagram of proposed mechanism showing aging‐related ECM remodeling through meningeal fibroblasts on the brain leptomeninges. Meningeal fibroblasts in the young brain showed dynamic COL1A1 synthetic and COL1‐interactive function on the collagen membrane. They showed ITGB1‐mediated adhesion on the COL1‐composed leptomeningeal membrane and induction of COL1A1 synthesis for maintaining the collagen membrane. With aging, meningeal fibroblasts showed depletion of COL1A1 synthetic function and altered cell–matrix interaction.
Figure 2. Representative rat meningeal images observed in the study. Compared to young rats, it was confirmed that type 1 collagen (COL1) decreased along with the accumulation of glycated end products (AGE) in the brain membrane of aged rats, and the activity of integrin beta 1 (ITGB1), a representative receptor corresponding to cell-collagen interaction. Instead, it was observed that the activity of discoidin domain receptor 2 (DDR2), one of the tyrosine kinases, increased.
Figure 3. Substance flux through the brain membrane decreases with aging. It was confirmed that the degree of adsorption of fluorescent substances contained in cerebrospinal fluid (CSF) to the brain membrane increased and the degree of entry into the periphery of the cerebral blood vessels decreased in the aged rats. In this study, only the influx into the brain was confirmed during the entry and exit of substances, but the degree of outflow will also be confirmed through future studies.
2023.03.15 View 4660 -
KAIST team develops smart immune system that can pin down on malignant tumors
A joint research team led by Professor Jung Kyoon Choi of the KAIST Department of Bio and Brain Engineering and Professor Jong-Eun Park of the KAIST Graduate School of Medical Science and Engineering (GSMSE) announced the development of the key technologies to treat cancers using smart immune cells designed based on AI and big data analysis. This technology is expected to be a next-generation immunotherapy that allows precision targeting of tumor cells by having the chimeric antigen receptors (CARs) operate through a logical circuit. Professor Hee Jung An of CHA Bundang Medical Center and Professor Hae-Ock Lee of the Catholic University of Korea also participated in this research to contribute joint effort.
Professor Jung Kyoon Choi’s team built a gene expression database from millions of cells, and used this to successfully develop and verify a deep-learning algorithm that could detect the differences in gene expression patterns between tumor cells and normal cells through a logical circuit. CAR immune cells that were fitted with the logic circuits discovered through this methodology could distinguish between tumorous and normal cells as a computer would, and therefore showed potentials to strike only on tumor cells accurately without causing unwanted side effects.
This research, conducted by co-first authors Dr. Joonha Kwon of the KAIST Department of Bio and Brain Engineering and Ph.D. candidate Junho Kang of KAIST GSMSE, was published by Nature Biotechnology on February 16, under the title Single-cell mapping of combinatorial target antigens for CAR switches using logic gates.
An area in cancer research where the most attempts and advances have been made in recent years is immunotherapy. This field of treatment, which utilizes the patient’s own immune system in order to overcome cancer, has several methods including immune checkpoint inhibitors, cancer vaccines and cellular treatments. Immune cells like CAR-T or CAR-NK equipped with chimera antigen receptors, in particular, can recognize cancer antigens and directly destroy cancer cells.
Starting with its success in blood cancer treatment, scientists have been trying to expand the application of CAR cell therapy to treat solid cancer. But there have been difficulties to develop CAR cells with effective killing abilities against solid cancer cells with minimized side effects. Accordingly, in recent years, the development of smarter CAR engineering technologies, i.e., computational logic gates such as AND, OR, and NOT, to effectively target cancer cells has been underway.
At this point in time, the research team built a large-scale database for cancer and normal cells to discover the exact genes that are expressed only from cancer cells at a single-cell level. The team followed this up by developing an AI algorithm that could search for a combination of genes that best distinguishes cancer cells from normal cells. This algorithm, in particular, has been used to find a logic circuit that can specifically target cancer cells through cell-level simulations of all gene combinations. CAR-T cells equipped with logic circuits discovered through this methodology are expected to distinguish cancerous cells from normal cells like computers, thereby minimizing side effects and maximizing the effects of chemotherapy.
Dr. Joonha Kwon, who is the first author of this paper, said, “this research suggests a new method that hasn’t been tried before. What’s particularly noteworthy is the process in which we found the optimal CAR cell circuit through simulations of millions of individual tumors and normal cells.” He added, “This is an innovative technology that can apply AI and computer logic circuits to immune cell engineering. It would contribute greatly to expanding CAR therapy, which is being successfully used for blood cancer, to solid cancers as well.”
This research was funded by the Original Technology Development Project and Research Program for Next Generation Applied Omic of the Korea Research Foundation.
Figure 1. A schematic diagram of manufacturing and administration process of CAR therapy and of cancer cell-specific dual targeting using CAR.
Figure 2. Deep learning (convolutional neural networks, CNNs) algorithm for selection of dual targets based on gene combination (left) and algorithm for calculating expressing cell fractions by gene combination according to logical circuit (right).
2023.03.09 View 6124 -
KAIST researchers develops a tech to enable production of ultrahigh-resolution LED with sub-micrometer scale pixels
Ultrahigh-resolution displays are an essential element for developing next-generation electronic products such as virtual reality (VR), augmented reality (AR), and smart watches, and can be applied not only to head-mounted displays, but also to smart glasses and smart lenses. The technology developed through this research is expected to be used to make such next-generation ultrahigh-resolution displays and other various sub-micro optoelectronic devices.
KAIST (President Kwang Hyung Lee) announced on the 22nd that Professor Yong-Hoon Cho's research team of KAIST Department of Physics developed the core technology for an ultrahigh resolution light-emitting diode (LED) display that can realize 0.5 micron-scale pixels smaller than 1/100 of the average hair thickness (about 100 microns) using focused ion beams.
Commonly, pixelation of ultrahigh-resolution LED displays usually relies on the etching method that physically cuts the area around the pixel, but as the pixel becomes smaller due to the occurrence of various defects around it, leading to side-effects of having leakage of current increased and light-emission efficiency decreased. In addition, various complex processes such as patterning for pixelation and post-processing for prevention of leakage current are required.
Professor Yong-Hoon Cho's research team developed a technology that can create pixels down to the size of a microscale without the complicated pre- and post-processing using a focused ion beam. This method has the advantage of being able to freely set the shape of the emitting pixel without causing any structural deformation on the material surface by controlling the intensity of the focused ion beam.
The focused ion beam technology has been widely used for ultrahigh-magnification imaging and nanostructure fabrication in fields such as materials engineering and biology. However, when a focused ion beam is used on a light emitting body such as an LED, light emission of a portion hit by the beam and a surrounding area rapidly decreases, which has been a barrier to fabricating a nano-scale light emitting structure. Upon facing this issue, Professor Cho's research team began the research on the idea that if they turned things around to use these problematic phenomena, they can be used in ultra-fine pixelation method on a sub-micron scale.
The research team used a focused ion beam whose intensity was softened to the extent that the surface was not shaved, and found that not only the light-emission rapidly decreased in the area hit by the focused ion beam, but also the local resistance greatly increased. As a result, while the surface of the LED is kept flat, the portion hit by the focused ion beam is optically and electrically isolated, enabling pixelation for independent operation.
Professor Yong-Hoon Cho, who led the research, said, “We have newly developed a technology that can create sub-micron-scale pixels without complicated processes using a focused ion beam, which will be a base technology that can be applied to next-generation ultrahigh-resolution displays and nano-photoelectronic devices.”
This research in which the Master's student Ji-Hwan Moon and the Ph.D. student Baul Kim of KAIST Department of Physics participated as co-first authors, was carried out with the support of the National Research Foundation of Korea's Support Program for Mid-Career Researchers and the Institute of Information and Communications Technology Planning and Evaluation. It was published online in 'Advanced Materials' on February 13, and was also selected as the internal cover of the next offline edition. (Title: Electrically Driven Sub-Micron Light-Emitting Diode Arrays Using Maskless and Etching-Free Pixelation)
Figure 1. Schematic diagram of the technology for ultrahigh density sub-micron-sized pixels through He focused ion beam (FIB) irradiation on an LED device
Figure 2. Ultra-high-density pixelation technology of micro light-emitting diodes (μLED) through He focused ion beam (FIB) irradiation
Figure 3. Rectangular pixels of different sizes (surface structure picture and luminescence picture) realized by a focused ion beam. Luminescence pictures of pixel arrays ranging in size from 20 µm x 20 µm to 0.5 µm x 0.5 µm, with surface flatness maintained.
2023.03.08 View 4790 -
KAIST researchers discovers the neural circuit that reacts to alarm clock
KAIST (President Kwang Hyung Lee) announced on the 20th that a research team led by Professor Daesoo Kim of the Department of Brain and Cognitive Sciences and Dr. Jeongjin Kim 's team from the Korea Institute of Science and Technology (KIST) have identified the principle of awakening animals by responding to sounds even while sleeping.
Sleep is a very important physiological process that organizes brain activity and maintains health. During sleep, the function of sensory nerves is blocked, so the ability to detect danger in the proximity is reduced. However, many animals detect approaching predators and respond even while sleeping. Scientists thought that animals ready for danger by alternating between deep sleep and light sleep.
A research team led by Professor Daesoo Kim at KAIST discovered that animals have neural circuits that respond to sounds even during deep sleep. While awake, the medial geniculate thalamus responds to sounds, but during deep sleep, or Non-REM sleep, the Mediodorsal thalamus responds to sounds to wake up the brain.
As a result of the study, when the rats fell into deep sleep, the nerves of the medial geniculate thalamus were also sleeping, but the nerves of mediodorsal thalamus were awake and responded immediately to sounds. In addition, it was observed that when mediodorsal thalamus was inhibited, the rats could not wake up even when a sound was heard, and when the mediodorsal thalamus was stimulated, the rats woke up within a few seconds without sound.
This is the first study to show that sleep and wakefulness can transmit auditory signals through different neural circuits, and was reported in the international journal, Current Biology on February 7, and was highlighted by Nature. (https://www.nature.com/articles/d41586-023-00354-0)
Professor Daesoo Kim explained, “The findings of this study can used in developing digital healthcare technologies to be used to improve understanding of disorders of senses and wakefulness seen in various brain diseases and to control the senses in the future.”
This research was carried out with the support from the National Research Foundation of Korea's Mid-Career Research Foundation Program.
Figure 1. Traditionally, sound signals were thought to be propagated from the auditory nerve to the auditory thalamus. However, while in slow-wave sleep, the auditory nerve sends sound signals to the mediodorsal thalamic neurons via the brainstem nerve to induce arousal in the brain.
Figure 2. GRIK4 dorsomedial nerve in response to sound stimulation. The awakening effect is induced as the activity of the GRIK4 dorsal medial nerve increases based on the time when sound stimulation is given.
2023.03.03 View 3823 -
KAIST presents a fundamental technology to remove metastatic traits from lung cancer cells
KAIST (President Kwang Hyung Lee) announced on January 30th that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering succeeded in using systems biology research to change the properties of carcinogenic cells in the lungs and eliminate both drug resistance and their ability to proliferate out to other areas of the body.
As the incidences of cancer increase within aging populations, cancer has become the most lethal disease threatening healthy life. Fatality rates are especially high when early detection does not happen in time and metastasis has occurred in various organs. In order to resolve this problem, a series of attempts were made to remove or lower the ability of cancer cells to spread, but they resulted in cancer cells in the intermediate state becoming more unstable and even more malignant, which created serious treatment challenges.
Professor Kwang-Hyun Cho's research team simulated various cancer cell states in the Epithelial-to-Mesenchymal Transition (EMT) of lung cancer cells, between epithelial cells without metastatic ability and mesenchymal cells with metastatic ability. A mathematical model of molecular network was established, and key regulators that could reverse the state of invasive and drug resistant mesenchymal cells back to the epithelial state were discovered through computer simulation analysis and molecular cell experiments. In particular, this process succeeded in properly reverting the mesenchymal lung cancer cells to a state where they were sensitive to chemotherapy treatment while avoiding the unstable EMT hybrid cell state in the middle process, which had remained a difficult problem.
The results of this research, in which KAIST Ph.D. student Namhee Kim, Dr. Chae Young Hwang, Researcher Taeyoung Kim, and Ph.D. student Hyunjin Kim participated, were published as an online paper in the international journal “Cancer Research” published by the American Association for Cancer Research (AACR) on January 30th. (Paper title: A cell fate reprogramming strategy reverses epithelial-to-mesenchymal transition of lung cancer cells while avoiding hybrid states)
Cells in an EMT hybrid state, which are caused by incomplete transitions during the EMT process in cancer cells, have the characteristics of both epithelial cells and mesenchymal cells, and are known to have high drug resistance and metastatic potential by acquiring high stem cell capacity. In particular, EMT is further enhanced through factors such as transforming growth factor-beta (TGF-β) secreted from the tumor microenvironment (TME) and, as a result, various cell states with high plasticity appear. Due to the complexity of EMT, it has been very difficult to completely reverse the transitional process of the mesenchymal cancer cells to an epithelial cell state in which metastatic ability and drug resistance are eliminated while avoiding the EMT hybrid cell state with high metastatic ability and drug resistance.
Professor Kwang-Hyun Cho's research team established a mathematical model of the gene regulation network that governs the complex process of EMT, and then applied large-scale computer simulation analysis and complex system network control technology to identify and verify 'p53', 'SMAD4', and 'ERK1' and 'ERK 2' (collectively ERKs) through molecular cell experiments as the three key molecular targets that can transform lung cancer cells in the mesenchymal cell state, reversed back to an epithelial cell state that no longer demonstrates the ability to metastasize, while avoiding the EMT hybrid cell state.
In particular, by analyzing the molecular regulatory mechanism of the complex EMT process at the system level, the key pathways were identified that were linked to the positive feedback that plays an important role in completely returning cancer cells to an epithelial cell state in which metastatic ability and drug resistance are removed.
This discovery is significant in that it proved that mesenchymal cells can be reverted to the state of epithelial cells under conditions where TGF-β stimulation are present, like they are in the actual environment where cancer tissue forms in the human body.
Abnormal EMT in cancer cells leads to various malignant traits such as the migration and invasion of cancer cells, changes in responsiveness to chemotherapy treatment, enhanced stem cell function, and the dissemination of cancer. In particular, the acquisition of the metastatic ability of cancer cells is a key determinant factor for the prognosis of cancer patients. The EMT reversal technology in lung cancer cells developed in this research is a new anti-cancer treatment strategy that reprograms cancer cells to eliminate their high plasticity and metastatic potential and increase their responsiveness to chemotherapy.
Professor Kwang-Hyun Cho said, "By succeeding in reversing the state of lung cancer cells that acquired high metastatic traits and resistance to drugs and reverting them to a treatable epithelial cell state with renewed sensitivity to chemotherapy, the research findings propose a new strategy for treatments that can improve the prognosis of cancer patients.”
Professor Kwang-Hyun Cho's research team was the first to present the principle of reversal treatment to revert cancer cells to normal cells, following through with the announcement of the results of their study that reverted colon cancer cells to normal colon cells in January of 2020, and also presenting successful re-programming research where the most malignant basal type breast cancer cells turned into less-malignant luminal type breast cancer cells that were treatable with hormonal therapies in January of 2022. This latest research result is the third in the development of reversal technology where lung cancer cells that had acquired metastatic traits returned to a state in which their metastatic ability was removed and drug sensitivity was enhanced.
This research was carried out with support from the Ministry of Science and ICT and the National Research Foundation of Korea's Basic Research in Science & Engineering Program for Mid-Career Researchers.
< Figure 1. Construction of the mathematical model of the regulatory network to represent the EMT phenotype based on the interaction between various molecules related to EMT.
(A) Professor Kwang-Hyun Cho's research team investigated numerous literatures and databases related to complex EMT, and based on comparative analysis of cell line data showing epithelial and mesenchymal cell conditions, they extracted key signaling pathways related to EMT and built a mathematical model of regulatory network (B) By comparing the results of computer simulation analysis and the molecular cell experiments, it was verified how well the constructed mathematical model simulated the actual cellular phenomena. >
< Figure 2. Understanding of various EMT phenotypes through large-scale computer simulation analysis and complex system network control technology.
(A) Through computer simulation analysis and experiments, Professor Kwang-Hyun Cho's research team found that complete control of EMT is impossible with single-molecule control alone. In particular, through comparison of the relative stability of attractors, it was revealed that the cell state exhibiting EMT hybrid characteristics has unstable properties. (B), (C) Based on these results, Prof. Cho’s team identified two feedbacks (positive feedback consisting of Snail-miR-34 and ZEB1-miR-200) that play an important role in avoiding the EMT hybrid state that appeared in the TGF-β-ON state. It was found through computer simulation analysis that the two feedbacks restore relatively high stability when the excavated p53 and SMAD4 are regulated. In addition, molecular cell experiments demonstrated that the expression levels of E-cad and ZEB1, which are representative phenotypic markers of EMT, changed similarly to the expression profile in the epithelial cell state, despite the TGF-β-ON state. >
< Figure 3. Complex molecular network analysis and discovery of reprogramming molecular targets for intact elimination of EMT hybrid features.
(A) Controlling the expression of p53 and SMAD4 in lung cancer cell lines was expected to overcome drug resistance, but contrary to expectations, chemotherapy responsiveness was not restored. (B) Professor Kwang-Hyun Cho's research team additionally analyzed computer simulations, genome data, and experimental results and found that high expression levels of TWIST1 and EPCAM were related to drug resistance. (C) Prof. Cho’s team identified three key molecular targets: p53, SMAD4 and ERK1 & ERK2. (D), (E) Furthermore, they identified a key pathway that plays an important role in completely reversing into epithelial cells while avoiding EMT hybrid characteristics, and confirmed through network analysis and attractor analysis that high stability of the key pathway was restored when the proposed molecular target was controlled. >
< Figure 4. Verification through experiments with lung cancer cell lines.
When p53 was activated and SMAD4 and ERK1/2 were inhibited in lung cancer cell lines, (A), (B) E-cad protein expression increased and ZEB1 protein expression decreased, and (C) mesenchymal cell status including TWIST1 and EPCAM and gene expression of markers related to stem cell potential characteristics were completely inhibited. In addition, (D) it was confirmed that resistance to chemotherapy treatment was also overcome as the cell state was reversed by the regulated target. >
< Figure 5. A schematic representation of the research results.
Prof. Cho’s research team identified key molecular regulatory pathways to avoid high plasticity formed by abnormal EMT of cancer cells and reverse it to an epithelial cell state through systems biology research. From this analysis, a reprogramming molecular target that can reverse the state of mesenchymal cells with acquired invasiveness and drug resistance to the state of epithelial cells with restored drug responsiveness was discovered.
For lung cancer cells, when a drug that enhances the expression of p53, one of the molecular targets discovered, and inhibits the expression of SMAD4 and ERK1 & ERK2 is administered, the molecular network of genes in the state of mesenchymal cells is modified, eventually eliminating metastatic ability and it is reprogrammed to turn into epithelial cells without the resistance to chemotherapy treatments. >
2023.01.30 View 11842 -
Afternoon chemotherapy proved to deliver more desirable results for female lymphoma patients
Chemotherapy is a commonly used regimen for cancer treatment, but it is also a double-edged sword. While the drugs are highly effective at killing cancer cells, they are also notorious for killing healthy cells in the body. As such, minimizing the drug’s damage to the patient’s body is necessary for improving the prognosis of chemotherapy.
Recently, “chrono-chemotherapy” have been gaining interest in the research community. As the name suggests, the aim is timing the delivery of the drugs when the body is least vulnerable to their harmful effects and while the cancer cells are at their most vulnerable.
< Figure 1. Chrono-chemotherapy considering circadian rhythm >
Chrono-chemotherapy exploits the fact that human physiological processes, including cell proliferation and differentiation, are regulated by an endogenous timer called the circadian clock. However, this has not been widely exploited in real-world clinical settings because, as of now, there is no systematic method for finding the optimal chemotherapy delivery time.
This problem was tackled by an interdisciplinary team of researchers from South Korea. They were led by principal investigators Jae Kyoung Kim (a mathematician from the Biomedical Mathematics Group, Institute for Basic Science) and Youngil Koh (an oncologist at Seoul National University Hospital). The researchers studied a group of patients suffering from diffuse large B-cell lymphoma (DLBCL).
Terminology
* Diffuse large B-cell lymphoma (DLBCL): Lymphoma is a type of blood cancer caused by the malignant transformation of lymphoid tissue cells. Lymphoma is divided into Hodgkin's lymphoma and non-Hodgkin's lymphoma (malignant lymphoma), and diffuse large B-cell lymphoma accounts for about 30 to 40% of non-Hodgkin's lymphoma.
The research team noticed that DLBCL patients at Seoul National University Hospital received chemotherapy on two different schedules, with some patients receiving morning treatment (8:30 a.m.) and others taking the drugs in the afternoon (2:30 p.m.). All patients received the same cancer treatment (R-CHOP), which is a combination of targeted therapy and chemotherapy, four to six times in the morning or afternoon at intervals of about three weeks.
They analyzed 210 patients to investigate whether there was any difference between morning and afternoon treatments. It was found that female patients who received the afternoon treatment had a 12.5 times reduced mortality rate (25% to 2%), while the cancer recurrence after 60 months decreased by 2.8 times (37% to 13%). In addition, chemotherapy side effects such as neutropenia were more common in female patients who received the morning treatment.
Surprisingly, there was no differences found in treatment efficiency depending on the treatment schedule in the cases of male patients.
To understand the cause of the gender differences, the research team analyzed upto 14,000 blood samples from the Seoul National University Hospital Health Examination Center. It was found that in females, white blood cell counts tended to decrease in the morning and increase in the afternoon. This indicates that the bone marrow proliferation rate was higher in the morning than in the afternoon because there is a upto 12 hour delay between bone marrow proliferation and blood cell production.
This means that if a female patient receives chemotherapy in the morning when bone marrow is actively producing blood cells, the possibility of adverse side effects becomes greater. These results are consistent with the findings from recent randomized clinical trials that showed female colorectal cancer patients treated with irinotecan in the morning suffered from higher drug toxicities.
One confounding variable was the drug dose. Since the morning female patients suffered from greater adverse side effects, oftentimes the dose had to be reduced for these patients. On average, the drug dose was reduced by upto 10% compared to the dose intensity given to female patients receiving the afternoon treatment.
Unlike the female patients, it was found that male patients did not show a significant difference in white blood cell count and bone marrow cell proliferation activity throughout the day, which explains why the timing of the treatment had no impact.
Professor Youngil Koh said, “We plan to verify the conclusions of this study again with a large-scale follow-up study that completely controls for the confounding variables, and to confirm whether chrono-chemotherapy has similar effects on other cancers.”
CI Jae Kyoung Kim said, “Because the time of the internal circadian clock can vary greatly depending on the individual's sleep-wake patterns, we are currently developing a technology to estimate a patient’s circadian clock from their sleep pattern. We hope that this can be used to develop an individualized anti-cancer chronotherapy schedule.”
< Figure 2. Chemotherapy in the afternoon can improve treatment outcomes. >
The daily fluctuation of proliferative activity of bone marrow is larger in females than in males, and it becomes higher in the morning (left). Thus, chemotherapy in the morning strongly inhibits proliferative activity in female lymphoma patients, resulting in a higher incidence of adverse events such as neutropenia and infections. This forced the clinicians to reduce the dose intensity (center). Consequently, female patients undergoing the morning treatment showed a lower survival probability than those undergoing the afternoon treatment (right). Specifically, only ~13% of female patients treated in the afternoon had a worse outcome and ~2% of them died while ~37% of female patients treated in the morning had a worse outcome and ~25% of them died. Male patients did not show any difference in treatment outcomes depending on the chemotherapy delivery time.
2023.01.27 View 4999 -
KAIST’s Robo-Dog “RaiBo” runs through the sandy beach
KAIST (President Kwang Hyung Lee) announced on the 25th that a research team led by Professor Jemin Hwangbo of the Department of Mechanical Engineering developed a quadrupedal robot control technology that can walk robustly with agility even in deformable terrain such as sandy beach.
< Photo. RAI Lab Team with Professor Hwangbo in the middle of the back row. >
Professor Hwangbo's research team developed a technology to model the force received by a walking robot on the ground made of granular materials such as sand and simulate it via a quadrupedal robot. Also, the team worked on an artificial neural network structure which is suitable in making real-time decisions needed in adapting to various types of ground without prior information while walking at the same time and applied it on to reinforcement learning. The trained neural network controller is expected to expand the scope of application of quadrupedal walking robots by proving its robustness in changing terrain, such as the ability to move in high-speed even on a sandy beach and walk and turn on soft grounds like an air mattress without losing balance.
This research, with Ph.D. Student Soo-Young Choi of KAIST Department of Mechanical Engineering as the first author, was published in January in the “Science Robotics”. (Paper title: Learning quadrupedal locomotion on deformable terrain).
Reinforcement learning is an AI learning method used to create a machine that collects data on the results of various actions in an arbitrary situation and utilizes that set of data to perform a task. Because the amount of data required for reinforcement learning is so vast, a method of collecting data through simulations that approximates physical phenomena in the real environment is widely used.
In particular, learning-based controllers in the field of walking robots have been applied to real environments after learning through data collected in simulations to successfully perform walking controls in various terrains.
However, since the performance of the learning-based controller rapidly decreases when the actual environment has any discrepancy from the learned simulation environment, it is important to implement an environment similar to the real one in the data collection stage. Therefore, in order to create a learning-based controller that can maintain balance in a deforming terrain, the simulator must provide a similar contact experience.
The research team defined a contact model that predicted the force generated upon contact from the motion dynamics of a walking body based on a ground reaction force model that considered the additional mass effect of granular media defined in previous studies.
Furthermore, by calculating the force generated from one or several contacts at each time step, the deforming terrain was efficiently simulated.
The research team also introduced an artificial neural network structure that implicitly predicts ground characteristics by using a recurrent neural network that analyzes time-series data from the robot's sensors.
The learned controller was mounted on the robot 'RaiBo', which was built hands-on by the research team to show high-speed walking of up to 3.03 m/s on a sandy beach where the robot's feet were completely submerged in the sand. Even when applied to harder grounds, such as grassy fields, and a running track, it was able to run stably by adapting to the characteristics of the ground without any additional programming or revision to the controlling algorithm.
In addition, it rotated with stability at 1.54 rad/s (approximately 90° per second) on an air mattress and demonstrated its quick adaptability even in the situation in which the terrain suddenly turned soft.
The research team demonstrated the importance of providing a suitable contact experience during the learning process by comparison with a controller that assumed the ground to be rigid, and proved that the proposed recurrent neural network modifies the controller's walking method according to the ground properties.
The simulation and learning methodology developed by the research team is expected to contribute to robots performing practical tasks as it expands the range of terrains that various walking robots can operate on.
The first author, Suyoung Choi, said, “It has been shown that providing a learning-based controller with a close contact experience with real deforming ground is essential for application to deforming terrain.” He went on to add that “The proposed controller can be used without prior information on the terrain, so it can be applied to various robot walking studies.”
This research was carried out with the support of the Samsung Research Funding & Incubation Center of Samsung Electronics.
< Figure 1. Adaptability of the proposed controller to various ground environments. The controller learned from a wide range of randomized granular media simulations showed adaptability to various natural and artificial terrains, and demonstrated high-speed walking ability and energy efficiency. >
< Figure 2. Contact model definition for simulation of granular substrates. The research team used a model that considered the additional mass effect for the vertical force and a Coulomb friction model for the horizontal direction while approximating the contact with the granular medium as occurring at a point. Furthermore, a model that simulates the ground resistance that can occur on the side of the foot was introduced and used for simulation. >
2023.01.26 View 11586 -
Overview of the 30-year history of metabolic engineering
< Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering at KAIST >
A research team comprised of Gi Bae Kim, Dr. So Young Choi, Dr. In Jin Cho, Da-Hee Ahn, and Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering at KAIST reported the 30-year history of metabolic engineering, highlighting examples of recent progress in the field and contributions to sustainability and health. Their paper “Metabolic engineering for sustainability and health” was published online in the 40th anniversary special issue of Trends in Biotechnology on January 10, 2023.
Metabolic engineering, a discipline of engineering that modifies cell phenotypes through molecular and genetic-level manipulations to improve cellular activities, has been studied since the early 1990s, and has progressed significantly over the past 30 years. In particular, metabolic engineering has enabled the engineering of microorganisms for the development of microbial cell factories capable of efficiently producing chemicals and materials as well as degrading recalcitrant contaminants.
This review article revisited how metabolic engineering has advanced over the past 30 years, from the advent of genetic engineering techniques such as recombinant DNA technologies to recent breakthroughs in systems metabolic engineering and data science aided by artificial intelligence. The research team highlighted momentous events and achievements in metabolic engineering, providing both trends and future directions in the field. Metabolic engineering’s contributions to bio-based sustainable chemicals and clean energy, health, and bioremediation were also reviewed. Finally, the research team shared their perspectives on the future challenges impacting metabolic engineering than must be overcome in order to achieve advancements in sustainability and health.
Distinguished Professor Sang Yup Lee said, “Replacing fossil resource-based chemical processes with bio-based sustainable processes for the production of chemicals, fuels, and materials using metabolic engineering has become our essential task for the future. By looking back on the 30+ years of metabolic engineering, we aimed to highlight the contributions of metabolic engineering to achieve sustainability and good health.” He added, “Metabolic engineering will play an increasingly important role as a key solution to the climate crisis, environmental pollution, food and energy shortages, and health problems in aging societies.”
< Figure: Metabolic Engineering Timeline >
2023.01.25 View 7609 -
Scientists re-writes FDA-recommended equation to improve estimation of drug-drug interaction
Drugs absorbed into the body are metabolized and thus removed by enzymes from several organs like the liver. How fast a drug is cleared out of the system can be affected by other drugs that are taken together because added substance can increase the amount of enzyme secretion in the body. This dramatically decreases the concentration of a drug, reducing its efficacy, often leading to the failure of having any effect at all. Therefore, accurately predicting the clearance rate in the presence of drug-drug interaction* is critical in the process of drug prescription and development of a new drug in order to ensure its efficacy and/or to avoid unwanted side-effects.
*Drug-drug interaction: In terms of metabolism, drug-drug interaction is a phenomenon in which one drug changes the metabolism of another drug to promote or inhibit its excretion from the body when two or more drugs are taken together. As a result, it increases the toxicity of medicines or causes loss of efficacy.
Since it is practically impossible to evaluate all interactions between new drug candidates and all marketed drugs during the development process, the FDA recommends indirect evaluation of drug interactions using a formula suggested in their guidance, first published in 1997, revised in January of 2020, in order to evaluate drug interactions and minimize side effects of having to use more than one type of drugs at once.
The formula relies on the 110-year-old Michaelis-Menten (MM) model, which has a fundamental limit of making a very broad and groundless assumption on the part of the presence of the enzymes that metabolizes the drug. While MM equation has been one of the most widely known equations in biochemistry used in more than 220,000 published papers, the MM equation is accurate only when the concentration of the enzyme that metabolizes the drug is almost non-existent, causing the accuracy of the equation highly unsatisfactory – only 38 percent of the predictions had less than two-fold errors.
“To make up for the gap, researcher resorted to plugging in scientifically unjustified constants into the equation,” Professor Jung-woo Chae of Chungnam National University College of Pharmacy said. “This is comparable to having to have the epicyclic orbits introduced to explain the motion of the planets back in the days in order to explain the now-defunct Ptolemaic theory, because it was 'THE' theory back then.”
< (From left) Ph.D. student Yun Min Song (KAIST, co-first authors), Professor Sang Kyum Kim (Chungnam National University, co-corresponding author), Jae Kyoung Kim, CI (KAIST, co-corresponding author), Professor Jung-woo Chae (Chungnam National University, co-corresponding author), Ph.D. students Quyen Thi Tran and Ngoc-Anh Thi Vu (Chungnam National University, co-first authors) >
A joint research team composed of mathematicians from the Biomedical Mathematics Group within the Institute for Basic Science (IBS) and the Korea Advanced Institute of Science and Technology (KAIST) and pharmacological scientists from the Chungnam National University reported that they identified the major causes of the FDA-recommended equation’s inaccuracies and presented a solution.
When estimating the gut bioavailability (Fg), which is the key parameter of the equation, the fraction absorbed from the gut lumen (Fa) is usually assumed to be 1. However, many experiments have shown that Fa is less than 1, obviously since it can’t be expected that all of the orally taken drugs to be completely absorbed by the intestines. To solve this problem, the research team used an “estimated Fa” value based on factors such as the drug’s transit time, intestine radius, and permeability values and used it to re-calculate Fg.
Also, taking a different approach from the MM equation, the team used an alternative model they derived in a previous study back in 2020, which can more accurately predict the drug metabolism rate regardless of the enzyme concentration. Combining these changes, the modified equation with re-calculated Fg had a dramatically increased accuracy of the resulting estimate. The existing FDA formula predicted drug interactions within a 2-fold margin of error at the rate of 38%, whereas the accuracy rate of the revised formula reached 80%.
“Such drastic improvement in drug-drug interaction prediction accuracy is expected to make great contribution to increasing the success rate of new drug development and drug efficacy in clinical trials. As the results of this study were published in one of the top clinical pharmacology journal, it is expected that the FDA guidance will be revised according to the results of this study.” said Professor Sang Kyum Kim from Chungnam National University College of Pharmacy.
Furthermore, this study highlights the importance of collaborative research between research groups in vastly different disciplines, in a field that is as dynamic as drug interactions.
“Thanks to the collaborative research between mathematics and pharmacy, we were able to recify the formula that we have accepted to be the right answer for so long to finally grasp on the leads toward healthier life for mankind.,” said Professor Jae Kyung Kim. He continued, “I hope seeing a ‘K-formula’ entered into the US FDA guidance one day.”
The results of this study were published in the online edition of Clinical Pharmacology and Therapeutics (IF 7.051), an authoritative journal in the field of clinical pharmacology, on December 15, 2022 (Korean time).
Thesis Title: Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction (doi: 10.1002/cpt.2824)
< Figure 1. The formula proposed by the FDA guidance for predicting drug-drug interactions (top) and the formula newly derived by the researchers (bottom). AUCR (the ratio of substrate area under the plasma concentration-time curve) represents the rate of change in drug concentration due to drug interactions. The research team more than doubled the accuracy of drug interaction prediction compared to the existing formula. >
< Figure 2. Existing FDA formulas tend to underestimate the extent of drug-drug interactions (gray dots) than the actual measured values. On the other hand, the newly derived equation (red dot) has a prediction rate that is within the error range of 2 times (0.5 to 2 times) of the measured value, and is more than twice as high as the existing equation. The solid line in the figure represents the predicted value that matches the measured value. The dotted line represents the predicted value with an error of 0.5 to 2 times. >
For further information or to request media assistance, please contact Jae Kyoung Kim at Biomedical Mathematics Group, Institute for Basic Science (IBS) (jaekkim@ibs.re.kr) or William I. Suh at the IBS Communications Team (willisuh@ibs.re.kr).
- About the Institute for Basic Science (IBS)
IBS was founded in 2011 by the government of the Republic of Korea with the sole purpose of driving forward the development of basic science in South Korea. IBS has 4 research institutes and 33 research centers as of January 2023. There are eleven physics, three mathematics, five chemistry, nine life science, two earth science, and three interdisciplinary research centers.
2023.01.18 View 9791