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Noninvasive Light-Sensitive Recombinase for Deep Brain Genetic Manipulation
A KAIST team presented a noninvasive light-sensitive photoactivatable recombinase suitable for genetic manipulation in vivo. The highly light-sensitive property of photoactivatable Flp recombinase will be ideal for controlling genetic manipulation in deep mouse brain regions by illumination with a noninvasive light-emitting diode. This easy-to-use optogenetic module made by Professor Won Do Heo and his team will provide a side-effect free and expandable genetic manipulation tool for neuroscience research.
Spatiotemporal control of gene expression has been acclaimed as a valuable strategy for identifying functions of genes with complex neural circuits. Studies of complex brain functions require highly sophisticated and robust technologies that enable specific labeling and rapid genetic modification in live animals. A number of approaches for controlling the activity of proteins or expression of genes in a spatiotemporal manner using light, small molecules, hormones, and peptides have been developed for manipulating intact circuits or functions.
Among them, recombination-employing, chemically inducible systems are the most commonly used in vivo gene-modification systems. Other approaches include selective or conditional Cre-activation systems within subsets of green fluorescent protein-expressing cells or dual-promoter-driven intersectional populations of cells.
However, these methods are limited by the considerable time and effort required to establish knock-in mouse lines and by constraints on spatiotemporal control, which relies on a limited set of available genetic promoters and transgenic mouse resources.
Beyond these constraints, optogenetic approaches allow the activity of genetically defined neurons in the mouse brain to be controlled with high spatiotemporal resolution. However, an optogenetic module for gene-manipulation capable of revealing the spatiotemporal functions of specific target genes in the mouse brain has remained a challenge.
In the study published at Nature Communication on Jan. 18, the team featured photoactivatable Flp recombinase by searching out split sites of Flp recombinase that were not previously identified, being capable of reconstitution to be active. The team validated the highly light-sensitive, efficient performance of photoactivatable Flp recombinase through precise light targeting by showing transgene expression within anatomically confined mouse brain regions.
The concept of local genetic labeling presented here suggests a new approach for genetically identifying subpopulations of cells defined by the spatial and temporal characteristics of light delivery. To date, an optogenetic module for gene-manipulation capable of revealing spatiotemporal functions of specific target genes in the mouse brain has remained out of reach and no such light-inducible Flp system has been developed. Accordingly, the team sought to develop a photoactivatable Flp recombinase that takes full advantage of the high spatiotemporal control offered by light stimulation.
This activation through noninvasive light illumination deep inside the brain is advantageous in that it avoids chemical or optic fiber implantation-mediated side effects, such as off-target cytotoxicity or physical lesions that might influence animal physiology or behaviors. The technique provides expandable utilities for transgene expression systems upon Flp recombinase activity in vivo, by designing a viral vector for minimal leaky expression influenced by viral nascent promoters.
The team demonstrated the utility of PA-Flp as a noninvasive in vivo optogenetic manipulation tool for use in the mouse brain, even applicable for deep brain structures as it can reach the hippocampus or medial septum using external LED light illumination.
The study is the result of five years of research by Professor Heo, who has led the bio-imaging and optogenetics fields by developing his own bio-imaging and optogenetics technologies. “It will be a great advantage to control specific gene expression desired by LEDs with little physical and chemical stimulation that can affect the physiological phenomenon in living animals,” he explained.
2019.01.22 View 9724 -
Ultrathin Digital Camera Inspired by Xenos Peckii Eyes
(Professor Ki-Hun Jeong from the Department of Bio and Brain Engineering)
The visual system of Xenos peckii, an endoparasite of paper wasps, demonstrates distinct benefits for high sensitivity and high resolution, differing from the compound eyes of most insects. Taking their unique features, a KAIST team developed an ultrathin digital camera that emulates the unique eyes of Xenos peckii.
The ultrathin digital camera offers a wide field of view and high resolution in a slimmer body compared to existing imaging systems. It is expected to support various applications, such as monitoring equipment, medical imaging devices, and mobile imaging systems.
Professor Ki-Hun Jeong from the Department of Bio and Brain Engineering and his team are known for mimicking biological visual organs. The team’s past research includes an LED lens based on the abdominal segments of fireflies and biologically inspired anti-reflective structures.
Recently, the demand for ultrathin digital cameras has increased, due to the miniaturization of electronic and optical devices. However, most camera modules use multiple lenses along the optical axis to compensate for optical aberrations, resulting in a larger volume as well as a thicker total track length of digital cameras. Resolution and sensitivity would be compromised if these modules were to be simply reduced in size and thickness.
To address this issue, the team have developed micro-optical components, inspired from the visual system of Xenos peckii, and combined them with a CMOS (complementary metal oxide semiconductor) image sensor to achieve an ultrathin digital camera.
This new camera, measuring less than 2mm in thickness, emulates the eyes of Xenos peckii by using dozens of microprism arrays and microlens arrays. A microprism and microlens pair form a channel and the light-absorbing medium between the channels reduces optical crosstalk. Each channel captures the partial image at slightly different orientation, and the retrieved partial images are combined into a single image, thereby ensuring a wide field of view and high resolution.
Professor Jeong said, “We have proposed a novel method of fabricating an ultrathin camera. As the first insect-inspired, ultrathin camera that integrates a microcamera on a conventional CMOS image sensor array, our study will have a significant impact in optics and related fields.”
This research, led by PhD candidates Dongmin Keum and Kyung-Won Jang, was published in Light: Science & Applications on October 24, 2018.
Figure 1. Natural Xenos peckii eye and the biological inspiration for the ultrathin digital camera (Light: Science & Applications 2018)
Figure 2. Optical images captured by the bioinspired ultrathin digital camera (Light: Science & Applications 2018)
2018.12.31 View 10945 -
Skin Hardness to Estimate Better Human Thermal Status
(Professor Young-Ho Cho and Researcher Sunghyun Yoon)
Under the same temperature and humidity, human thermal status may vary due to individual body constitution and climatic environment. A KAIST research team previously developed a wearable sweat rate sensor for human thermal comfort monitoring. Furthering the development, this time they proposed skin hardness as an additional, independent physiological sign to estimate human thermal status more accurately. This novel approach can be applied to developing systems incorporating human-machine interaction, which requires accurate information about human thermal status.
Professor Young-Ho Cho and his team from the Department of Bio and Brain Engineering had previously studied skin temperature and sweat rate to determine human thermal comfort, and developed a watch-type sweat rate sensor that accurately and steadily detects thermal comfort last February (title: Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring ).
However, skin temperature and sweat rate are still not enough to estimate exact human thermal comfort. Hence, an additional indicator is required for enhancing the accuracy and reliability of the estimation and the team selected skin hardness. When people feel hot or cold, arrector pili muscles connected to hair follicles contract and expand, and skin hardness comes from this contraction and relaxation of the muscles. Based on the phenomenon of changing skin hardness, the team proposed skin hardness as a new indicator for measuring human thermal sensation.
With this new estimation model using three physiological signs for estimating human thermal status, the team conducted human experiments and verified that skin hardness is effective and independent from the two conventional physiological signs. Adding skin hardness to the conventional model can reduce errors by 23.5%, which makes its estimation more reliable.
The team will develop a sensor that detects skin hardness and applies it to cognitive air-conditioning and heating systems that better interact with humans than existing systems.
Professor Cho said, “Introducing this new indicator, skin hardness, elevates the reliability of measuring human thermal comfort regardless of individual body constitution and climatic environment. Based on this method, we can develop a personalized air conditioning and heating system that will allow affective interaction between humans and machines by sharing both physical and mental health conditions and emotions.”
This research, led by researchers Sunghyun Yoon and Jai Kyoung Sim, was published in Scientific Reports, Vol.8, Article No.12027 on August 13, 2018. (pp.1-6)
Figure 1. Measuring human thermal status through skin hardness
Figure 2. The instrument used for measuring human thermal status through skin hardness
2018.10.17 View 8410 -
Trigger of the Hyperactivation of Fibrosis Identified
(Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering)
Scientists have been investigating the negative effects that the hyperactivation of fibrosis has on fibrotic diseases and cancer. A KAIST research team unveiled a positive feedback loop that bi-stably activates fibroblasts in collaboration with Samsung Medical Center. This finding will contribute to developing therapeutic targets for both fibrosis and cancer.
Human fibroblasts are dormant in normal tissue, but show radical activation during wound healing. However, the principle that induces their explosive activation has not yet been identified.
Here, Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering, in collaboration with Professor Seok-Hyung Kim from Samsung Medical Center, discovered the principle of a circuit that continuously activates fibroblasts.
They constructed a positive feedback loops (PFLs) where Twist1, Prrx1, and Tenascin-C (TNC) molecules consecutively activate fibroblasts. They confirmed that these are the main inducers of fibroblast activation by conducting various experiments, including molecular biological tests, mathematical modeling, animal testing, and computer simulations to conclude that they are the main inducers of fibroblast activation.
According to their research, Twist 1 is a key regulator of cancer-associated fibroblasts, which directly upregulates Prrx1 and then triggers TNC, which also increases Twist1 expression. This circuit consequently forms a Twist-Prrx1-TNC positive feedback loop.
Activated fibroblasts need to be deactivated after wounds are healed. However, if the PFLs continue, the fibroblasts become the major cause of worsening fibrotic diseases and cancers.
Therefore, the team expects that Twist1-Prrx1-TNC positive PFLs will be applied for novel and effective therapeutic targeting of fibrotic diseases and cancers.
This research was published in Nature Communications on August 1, 2018.
Figure 1. Twist1 increases tenascin-c expression in cancer-associated fibroblasts. Twist1 is a potent but indirect inducer of tenascin-c (TNC), which is essential for maintaining Twist1 expression in cancer-associated fibroblasts (CAFs).
Figure 2. Summary of the study. The Twist1-Prrx1-TNC positive feedback regulation provides clues for understanding the activation of fibroblasts during wound healing under normal conditions, as well as abnormally activated fibroblasts in pathological conditions such as cancerous and fibrotic diseases. Under normal conditions, the PFL acts as a reversible bistable switch by which the activation of fibroblasts is “ON" above a sufficient level of stimulation and “OFF" for the withdrawal of the stimulus. However, this switch can be permanently turned on under pathologic conditions by continued activation of the PFL, resulting in sustained proliferation of fibroblasts.
2018.10.11 View 8014 -
Mathematical Principle behind AI's 'Black Box'
(from left: Professor Jong Chul Ye, PhD candidates Yoseob Han and Eunju Cha)
A KAIST research team identified the geometrical structure of artificial intelligence (AI) and discovered the mathematical principles of highly performing artificial neural networks, which can be applicable in fields such as medical imaging.
Deep neural networks are an exemplary method of implementing deep learning, which is at the core of the AI technology, and have shown explosive growth in recent years. This technique has been used in various fields, such as image and speech recognition as well as image processing.
Despite its excellent performance and usefulness, the exact working principles of deep neural networks has not been well understood, and they often suffer from unexpected results or errors. Hence, there is an increasing social and technical demand for interpretable deep neural network models.
To address these issues, Professor Jong Chul Ye from the Department of Bio & Brain Engineering and his team attempted to find the geometric structure in a higher dimensional space where the structure of the deep neural network can be easily understood. They proposed a general deep learning framework, called deep convolutional framelets, to understand the mathematical principle of a deep neural network in terms of the mathematical tools in Harmonic analysis.
As a result, it was found that deep neural networks’ structure appears during the process of decomposition of high dimensionally lifted signal via Hankel matrix, which is a high-dimensional structure formerly studied intensively in the field of signal processing.
In the process of decomposing the lifted signal, two bases categorized as local and non-local basis emerge. The researchers found that non-local and local basis functions play a role in pooling and filtering operation in convolutional neural network, respectively.
Previously, when implementing AI, deep neural networks were usually constructed through empirical trial and errors. The significance of the research lies in the fact that it provides a mathematical understanding on the neural network structure in high dimensional space, which guides users to design an optimized neural network.
They demonstrated improved performance of the deep convolutional framelets’ neural networks in the applications of image denoising, image pixel in painting, and medical image restoration.
Professor Ye said, “Unlike conventional neural networks designed through trial-and-error, our theory shows that neural network structure can be optimized to each desired application and are easily predictable in their effects by exploiting the high dimensional geometry. This technology can be applied to a variety of fields requiring interpretation of the architecture, such as medical imaging.”
This research, led by PhD candidates Yoseob Han and Eunju Cha, was published in the April 26th issue of the SIAM Journal on Imaging Sciences.
Figure 1. The design of deep neural network using mathematical principles
Figure 2. The results of image noise cancelling
Figure 3. The artificial neural network restoration results in the case where 80% of the pixels are lost
2018.09.12 View 7632 -
Flexible Drug Delivery Microdevice to Advance Precision Medicine
(Schematic view of flexible microdevice: The flexible drug delivery device for controlled release fabricated via inorganic laser lift off.)
A KAIST research team has developed a flexible drug delivery device with controlled release for personalized medicine, blazing the path toward theragnosis.
Theragnosis, an emerging medical technology, is gaining attention as key factor to advance precision medicine for its featuring simultaneous diagnosis and therapeutics. Theragnosis devices including smart contact lenses and microneedle patches integrate physiological data sensors and drug delivery devices. The controlled drug delivery boasts fewer side-effects, uniform therapeutic results, and minimal dosages compared to oral ingestion. Recently, some research groups conducted in-human applications of controlled-release bulky microchips for osteoporosis treatment. However they failed to demonstrate successful human-friendly flexible drug delivery systems for controlled release.
For this microdevice, the team under Professor Daesoo Kim from the Department of Biological Science and Professor Keon Jae Lee from the Department of Materials Science and Engineering, fabricated a device on a rigid substrate and transferred a 50 µm-thick active drug delivery layer to the flexible substrate via inorganic laser lift off. The fabricated device shows mechanical flexibility while maintaining the capability of precise administration of exact dosages at desired times. The core technology is to produce a freestanding gold capping layer directly on top of the microreservoir with the drugs inside, which had been regarded as impossible in conventional microfabrication.
The developed flexible drug delivery system can be applied to smart contact lenses or the brain disease treatments by implanting them into cramped and corrugated organs. In addition, when powered wirelessly, it will represent a novel platform for personalized medicine. The team already proved through animal experimentation that treatment for brain epilepsy made progress by releasing anti-epileptic medication through the device.
Professor Lee believes the flexible microdevice will further expand the applications of smart contact lenses, therapeutic treatments for brain disease, and subcutaneous implantations for daily healthcare system. This study “Flexible Wireless Powered Drug Delivery System for Targeted Administration on Cerebral Cortex” was described in the June online issue of Nano Energy.
(Photo: The flexible drug delivery device for contolled relase attached on a glass rod.)
2018.08.13 View 11286 -
How to Trigger Innate Fear Response?
(Figure:This illustration describes how ACC-BLA circuit controls innate freezing response depending on its activity level.)
When animals encounter danger, they usually respond to the situation in one of two ways: to freeze or to flee. How do they make this quick decision in a life or death moment?
According to KAIST neuroscientists, there are two types of fear: learned versus innate. The latter is known to be induced without any prior experience and is thus naturally encoded in the brain. A research team under Professor Jin-Hee Han in the Department of Biological Sciences identified the brain circuit responsible for regulating the innate fear response.
The study, which appeared in the July 24 issue of Nature Communications represents a significant step toward understanding how the neural circuits in the prefrontal cortex create behavioral responses to external threats. This also represents a new paradigm in therapeutic development for fear-related mental disorders.
Responses of freezing or fleeing when facing external threats reflect behavioral and physiological changes in an instinctive move to adapt to the new environment for survival. These responses are controlled by the emotional circuit systems of the brain and the malfunction of this circuit leads to fear-related disorders.
The anterior cingulate cortex (ACC) is a sub-region within the prefrontal cortex, comprising a part of the brain circuitry that regulates behavioral and physiological fear responses. This area is capable of high-order processing of the perceived sensory information and conveys ‘top-down’ information toward the amygdala and brainstem areas, known as the response outlet.
Many studies have already demonstrated that the brain regions in the prefrontal cortex regulate the response against learned threats. However, it has been unknown how innate responses against fear are encoded in the neural circuits in the prefrontal cortex.
Dr. Jinho Jhang, the lead author of the study explains how the team achieved their key idea. “Many overseas studies have already proved that the prefrontal cortex circuit works to regulate the fear response. However, researchers have paid little attention to the innate response against predators. Professor Han suggested we do research on the instinctive fear response instead of the learned response. We particularly focused on the anterior cingulate region, which has been connected with memory, pain, and sympathy, but not the fear response itself. Since we turned in this new direction, we have accumulated some significant data,” said Dr. Jhang.
For this study, Professor Han’s team investigated how mice react when exposed to the olfactory stimuli of predators. Based on the results of optogenetic manipulation, neural circuit tracing, and ex vivo slice electrophysiology experiments, the team demonstrated that the anterior cingulate cortex and its projection input to the basolateral amygdala play a role in the inhibitory regulation of innate fear responses to predators’ odors in mice.
Professor Han believes these results will extend the understanding of how instinctive fear responses can be encoded in our brain circuits. “Our findings will help to develop therapeutic treatments for mental disorders aroused from fear such as panic disorders and post-traumatic stress disorder,” said Professor Han.
2018.08.08 View 9269 -
Animal Cyborg: Behavioral Control by 'Toy' Craving Circuit
Children love to get toys from parents for their birthday present. This craving toward items also involves object hoarding disorders and shopping addiction. However, the biological meaning of why the brain pursues objects or items has remained unknown. Part of the answer may lie with a neural circuit in the hypothalamus associated with “object craving,” says neuroscientist Daesoo Kim from the Department of Biological Sciences at KAIST.
His research team found that some neurons in the hypothalamus are activated during playing with toys in mice. Thanks to optogenetics, they proved that these neurons in the hypothalamus actually governs obsessive behavior toward non-food objects in mice.
“When we stimulate a neuron in the hypothalamus of mice, they anxiously chased target objects. We found evidence that the neural circuits in the medial preoptic area (MPA) modulate “object craving,” the appetite for possessing objects” said Professor Kim.
Researchers also proved that the MPA circuit facilitate hunting behavior in response to crickets, a natural prey to mice, showing the role of this circuit for catching prey. Further, the MPA nerves send excitatory signals to the periaqueductal gray (PAG), located around the cerebral aqueduct, to create such behavior. The team named this circuit the ‘MPA-PAG’ circuit.
The team showed that they could control mammalian behavior for the first time with this scheme of MPA-Induced Drive Assisted Steering (MIDAS), in which a mouse chase the target objects in the front of head during stimulation of the MPA-PAG circuit. MIDAS allows mice to overcome obstacles to move in a desired path using optogenetics.
(Professor Daesoo Kim)
Professor Kim, who teamed up with Professor Phill Seung Lee in the Department of Mechanical Engineering, explained the significance of the research, “This study provides evidence to treat brain disorders such as compulsive hoarding and kleptomania. It also contributes to the development of technology to control the behavior of animals and humans using strong innate motivation, and thus could impact neuro-economics, defense, and disaster relief.”
He said the team would like to complete the neural circuit map governing behaviors of possession and hunting in the near future by exploring correlations with other neural behaviors controlling possessing and hunting activities.
This research was funded by the Samsung Science and Technology Foundation and published in Nature Neuroscience in March 2018.
(Figure 1: Schematics showing possessive behavior induced by the MPA neural circuit)
(Figure 2: Schematics of the MIDAS system that controls mammals behavior using the desire to possess. A MIDAS mouse is following the bait object controlled wirelessly.)
2018.04.23 View 12493 -
Printed Thermo-Plasmonic Heat Patterns for Neurological Disorder Treatment
(Professor Nam and Dr. Kang, right)
A KAIST team presented a highly customizable neural stimulation method. The research team developed a technology that can print the heat pattern on a micron scale to enable the control of biological activities remotely.
The researchers integrated a precision inkjet printing technology with bio-functional thermo-plasmonic nanoparticles to achieve a ‘selective nano-photothermal neural stimulation method.’
The research team of Professor Yoonkey Nam at the Department of Bio and Brain Engineering expects this will serve as an enabling technology for personalized precision neuromodulation therapy for patients with neurological disorders.
The nano-photothermal neural stimulation method uses the thermo-plasmonic effect of metal nanoparticles to modulate the activities of neuronal networks. With the thermo-plasmonic effect, metal nanoparticles can absorb specific wavelength of illuminated light to efficiently generate localized heat. The research team discovered the inhibitory behavior of spontaneous activities of neurons upon photothermal stimulation four years ago. Since then, they have developed this technology to control hyperactive behaviors of neurons and neural circuits, which is often found in neurological disorders such as epilepsy.
In order to overcome the limitation on the spatial selectivity and resolution of the previously developed nano-photothermal method, the team adopted an inkjet printing technology to micro pattern the plasmonic nanoparticles (a few tens of microns), and successfully demonstrated that the nano-photothermal stimulation can be selectively applied according to the printed patterns.
The researchers applied a polyelectrolyte layer-by-layer coating method to printing substrates in a way to improve the pattern fidelity and achieve the uniform assembly of nanoparticles. The electrostatic attraction between the printed nanoparticles and the coated printing substrate also helped the stability of the attached nanoparticles. Because the polyelectrolyte coating is biocompatible, biological experiments including cell culture are possible with the technology developed in this work.
Using printed gold nanorod particles in a few tens of microns resolution over a several centimeters area, the researchers showed that highly complex heat patterns can be precisely formed upon light illumination according to the printing image.
Lastly, the team confirmed that the printed heat patterns can selectively and instantaneously inhibit the activities of cultured hippocampal neurons upon near-infrared light illumination. Because the printing process is applicable to thin and flexible substrates, the technology can be easily applied to implantable neurological disorder treatment devices and wearable devices. By selectively applying the heat patterns to only the desired cellular areas, customized and personalized photothermal neuromodulation therapy can be applied to patients.
“The fact that any desired heat patterns can be simply ‘printed’ anywhere broadens the applicability of this technology in many engineering fields. In bioengineering, it can be applied to neural interfaces using light and heat to modulate physiological functions. As another engineering application, for example, printed heat patterns can be used as a new concept of anti-counterfeit applications,” said the principal investigator, Yoonkey Nam at KAIST. This work, led mainly by Dr. Hongki Kang, was published in ACS Nano on February 5th 2018.
2018.04.06 View 8469 -
Finding Human Thermal Comfort with a Watch-type Sweat Rate Sensor
(from left: Professor Young-Ho Cho and Researcher SungHyun Yoon)
KAIST developed a watch-type sweat rate sensor. This subminiature device can detect human thermal comfort accurately and steadily by measuring an individual’s sweat rate.
It is natural to sweat more in the summer and less in the winter; however, an individual’s sweat rate may vary in a given environment. Therefore, sweat can be an excellent proxy for sensing core body temperature.
Conventional sweat rate sensors using natural ventilation require bulky external devices, such as pumps and ice condensers. They are usually for physiological experiments, hence they need a manual ventilation process or high power, bulky thermos-pneumatic actuators to lift sweat rate detection chambers above skin for continuous measurement. There is also a small sweat rate sensor, but it needs a long recovery period.
To overcome these problems, Professor Young-Ho Cho and his team from the Department of Bio and Brain Engineering developed a lightweight, watch-type sweat sensor. The team integrated miniaturized thermos-pneumatic actuators for automatic natural ventilation, which allows sweat to be measured continuously.
This watch-type sensor measures sweat rate with the humidity rising rate when the chamber is closed during skin contact. Since the team integrated thermos-pneumatic actuators, the chamber no longer needs to be separated manually from skin after each measurement in order for the chamber to ventilate the collected humidity.
Moreover, this sensor is wind-resistant enough to be used for portable and wearable devices. The team identified that the sensor operates steadily with air velocity ranging up to 1.5m/s, equivalent to the average human walking speed.
Although this subminiature sensor (35mm x 25mm) only weighs 30 grams, it operates continuously for more than four hours using the conventional wrist watch batteries.
The team plans to utilize this technology for developing a new concept of cognitive air-conditioning systems recognizing Human thermal status directly; while the conventional air-conditioning systems measuring air temperature and humidity.
Professor Cho said, “Our sensor for human thermal comfort monitoring can be applied to customized or smart air conditioners. Furthermore, there will be more demands for both physical and mental healthcare, hence this technology will serve as a new platform for personalized emotional communion between humans and devices.”
This research, led by researchers Jai Kyoung Sim and SungHyun Yoon, was published in Scientific Reports on January 19, 2018.
Figure1. The fabricated watch-type sweat rate sensor for human thermal comfort monitoring
Figure 2. Views of the watch-type sweat rate sensor
Figure 3. Operation of the watch-type sweat rate sensor
2018.02.08 View 10701 -
Technology to Find Optimum Drug Target for Cancer Developed
(Professor Kwang-Hyun Cho (right) and lead author Dr. Minsoo Choi)
A KAIST research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering developed technology to find the optimum drug target according to the type of cancer cell. The team used systems biology to analyze molecular network dynamics that reflect genetic mutations in cancer cells and to predict drug response. The technology could contribute greatly to future anti-cancer drug development.
There are many types of genetic variations found in cancer cells, including gene mutations and copy number variations. These variations differ in cancer cells even within the same type of cancer, and thus the drug response varies cell by cell. Cancer researchers worked towards identifying frequently occurring genetic variations in cancer patients and, in particular, the mutations that can be used as an index for specific drugs. Previous studies focused on identifying a single genetic mutation or creating an analysis of the structural characteristics of a gene network. However, this approach was limited in its inability to explain the biological properties of cancer which are induced by various gene and protein interactions in cancer cells, which result in differences in drug response.
Gene mutations in cancer cells not only affect the function of the affected gene, but also other genes that interact with the mutated gene and proteins. As a consequence, one mutation could lead to changes in the dynamical properties of the molecular network. Therefore, the responses to anti-cancer drugs by cancer cells differ. The current treatment approach that ignores molecular network dynamics and targets a few cancer-related genes is only effective on a fraction of patients, while many other patients exhibit resistance to the drug.
Professor Cho’s team integrated a large-scale computer simulation using super-computing and cellular experiments to analyze changes in molecular network dynamics in cancer cells.
This led to development of technology to find the optimum drug target according to the type of cancer cells by predicting drug response. This technology was applied to the molecular network of known tumor suppressor p53. The team used large-scale cancer cell genomic data available from The Cancer Cell Line Encyclopedia (CCLE) to construct different molecular networks specific to the characteristics of genetic variations.
Perturbation analysis on drug response in each molecular network was used to quantify changes in cancer cells from drug response and similar networks were clustered. Then, computer simulations were used to analyze the synergetic effects in terms of efficacy and combination to predict the level of drug response. Based on the simulation results from various cancer cell lines including lung, breast, bone, skin, kidney, and ovary cancers were used in drug response experiments for compare analysis.
This technique can be applied in any molecular network to identify the optimum drug target for personalized medicine.
The research team suggests that the technology can analyze varying drug response due to the heterogeneity of cancer cells by considering the overall modulatory interactions rather than focusing only on a specific gene or protein. Further, the technology aids the prediction of causes of drug resistance and thus the identification of the optimum drug target to inhibit the resistance. This could be core source technology that can be used in drug repositioning, a process of applying existing drugs to new disease targets.
Professor Cho said, “Genetic variations in cancer cells are the cause of diverse drug response, but a complete analysis had not yet been made.” He continued, “Systems biology allowed the simulation of drug responses by cancer cell molecular networks to identify fundamental principles of drug response and optimum drug targets using a new conceptual approach.”
This research was published in Nature Communications on December 5 and was funded by Ministry of Science and ICT and National Research Foundation of Korea.
(Figure 1. Drug response prediction for each cancer cell type from computer simulation and cellular experiment verification for comparison)
(Figure 2. Drug response prediction based on cancer cell molecular network dynamics and clustering of cancer cells by their molecular networks)
(Figure 3. Identification of drug target for each cancer cell type by cellular molecular network analysis and establishment for personalized medicine strategy for each cancer patient)
2017.12.15 View 8462 -
Mutant Gene Network in Colon Cancer Identified
The principles of the gene network for colon tumorigenesis have been identified by a KAIST research team. The principles will be used to find the molecular target for effective anti-cancer drugs in the future. Further, this research gained attention for using a systems biology approach, which is an integrated research area of IT and BT.
The KAIST research team led by Professor Kwang-Hyun Cho for the Department of Bio and Brain Engineering succeeded in the identification. Conducted by Dr. Dongkwan Shin and student researchers Jonghoon Lee and Jeong-Ryeol Gong, the research was published in Nature Communications online on November 2.
Human cancer is caused by genetic mutations. The frequency of the mutations differs by the type of cancer; for example, only around 10 mutations are found in leukemia and childhood cancer, but an average of 50 mutations are found in adult solid cancers and even hundreds of mutations are found in cancers due to external factors, such as with lung cancer.
Cancer researchers around the world are working to identify frequently found genetic mutations in patients, and in turn identify important cancer-inducing genes (called ‘driver genes’) to develop targets for anti-cancer drugs. However, gene mutations not only affect their own functions but also affect other genes through interactions. Therefore, there are limitations in current treatments targeting a few cancer-inducing genes without further knowledge on gene networks, hence current drugs are only effective in a few patients and often induce drug resistance.
Professor Cho’s team used large-scale genomic data from cancer patients to construct a mathematical model on the cooperative effects of multiple genetic mutations found in gene interaction networks. The basis of the model construction was The Cancer Genome Atlas (TCGA) presented at the International Cancer Genome Consortium. The team successfully quantified the effects of mutations in gene networks to group colon cancer patients by clinical characteristics.
Further, the critical transition phenomenon that occurs in tumorigenesis was identified using large-scale computer simulation analysis, which was the first hidden gene network principle to be identified. Critical transition is the phenomenon in which the state of matter is suddenly changed through phase transition. It was not possible to identify the presence of transition phenomenon in the past, as it was difficult to track the sequence of gene mutations during tumorigenesis.
The research team used a systems biology-based research method to find that colon cancer tumorigenesis shows a critical transition phenomenon if the known driver gene mutations follow sequentially. Using the developed mathematical model, it can be possible to develop a new anti-cancer targeting drug that most effectively inhibits the effects of many gene mutations found in cancer patients. In particular, not only driver genes, but also other passenger genes affected by the gene mutations, could be evaluated to find the most effective drug targets.
Professor Cho said, “Little was known about the contribution of many gene mutations during tumorigenesis.” He continued, “In this research, a systems biology approach identified the principle of gene networks for the first time to suggest the possibility of anti-cancer drug target identification from a new perspective.”
This research was funded by the Ministry of Science and ICT and the National Research Foundation of Korea.
Figure1. Formation of giant clusters via mutation propagation
Figure2. Critical transition phenomenon by cooperative effect of mutations in tumorigenesis
2017.11.10 View 9596