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The Dynamic Tracking of Tissue-Specific Secretory Proteins
Researchers develop a versatile and powerful tool for studying the spatiotemporal dynamics of secretory proteins, a valuable class of biomarkers and therapeutic targets Researchers have presented a method for profiling tissue-specific secretory proteins in live mice. This method is expected to be applicable to various tissues or disease models for investigating biomarkers or therapeutic targets involved in disease progression. This research was reported in Nature Communications on September 1. Secretory proteins released into the blood play essential roles in physiological systems. They are core mediators of interorgan communication, while serving as biomarkers and therapeutic targets. Previous studies have analyzed conditioned media from culture models to identify cell type-specific secretory proteins, but these models often fail to fully recapitulate the intricacies of multi-organ systems and thus do not sufficiently reflect biological realities. These limitations provided compelling motivation for the research team led by Jae Myoung Suh and his collaborators to develop techniques that could identify and resolve characteristics of tissue-specific secretory proteins along time and space dimensions. For addressing this gap in the current methodology, the research team utilized proximity-labeling enzymes such as TurboID to label secretory proteins in endoplasmic reticulum lumen using biotin. Thereafter, the biotin-labeled secretory proteins were readily enriched through streptavidin affinity purification and could be identified through mass spectrometry. To demonstrate its functionality in live mice, research team delivered TurboID to mouse livers via an adenovirus. After administering the biotin, only liver-derived secretory proteins were successfully detected in the plasma of the mice. Interestingly, the pattern of biotin-labeled proteins secreted from the liver was clearly distinctive from those of hepatocyte cell lines. First author Kwang-eun Kim from the Graduate School of Medical Science and Engineering explained, “The proteins secreted by the liver were significantly different from the results of cell culture models. This data shows the limitations of cell culture models for secretory protein study, and this technique can overcome those limitations. It can be further used to discover biomarkers and therapeutic targets that can more fully reflect the physiological state.” This work research was supported by the National Research Foundation of Korea, the KAIST Key Research Institutes Project (Interdisciplinary Research Group), and the Institute for Basic Science in Korea. -PublicationKwang-eun Kim, Isaac Park et al., “Dynamic tracking and identification of tissue-specific secretory proteins in the circulation of live mice,” Nature Communications on Sept.1, 2021(https://doi.org/10.1038/s41467-021-25546-y) -ProfileProfessor Jae Myoung Suh Integrated Lab of Metabolism, Obesity and Diabetes Researchhttps://imodkaist.wixsite.com/home Graduate School of Medical Science and Engineering College of Life Science and BioengineeringKAIST
A Mathematical Model Reveals Long-Distance Cell Communication Mechanism
How can tens of thousands of people in a large football stadium all clap together with the same beat even though they can only hear the people near them clapping? A combination of a partial differential equation and a synthetic circuit in microbes answers this question. An interdisciplinary collaborative team of Professor Jae Kyoung Kim at KAIST, Professor Krešimir Josić at the University of Houston, and Professor Matt Bennett at Rice University has identified how a large community can communicate with each other almost simultaneously even with very short distance signaling. The research was reported at Nature Chemical Biology. Cells often communicate using signaling molecules, which can travel only a short distance. Nevertheless, the cells can also communicate over large distances to spur collective action. The team revealed a cell communication mechanism that quickly forms a network of local interactions to spur collective action, even in large communities. The research team used an engineered transcriptional circuit of combined positive and negative feedback loops in E. coli, which can periodically release two types of signaling molecules: activator and repressor. As the signaling molecules travel over a short distance, cells can only talk to their nearest neighbors. However, cell communities synchronize oscillatory gene expression in spatially extended systems as long as the transcriptional circuit contains a positive feedback loop for the activator. Professor Kim said that analyzing and understanding such high-dimensional dynamics was extremely difficult. He explained, “That’s why we used high-dimensional partial differential equation to describe the system based on the interactions among various types of molecules.” Surprisingly, the mathematical model accurately simulates the synthesis of the signaling molecules in the cell and their spatial diffusion throughout the chamber and their effect on neighboring cells. The team simplified the high-dimensional system into a one-dimensional orbit, noting that the system repeats periodically. This allowed them to discover that cells can make one voice when they lowered their own voice and listened to the others. “It turns out the positive feedback loop reduces the distance between moving points and finally makes them move all together. That’s why you clap louder when you hear applause from nearby neighbors and everyone eventually claps together at almost the same time,” said Professor Kim. Professor Kim added, “Math is a powerful as it simplifies complex thing so that we can find an essential underlying property. This finding would not have been possible without the simplification of complex systems using mathematics." The National Institutes of Health, the National Science Foundation, the Robert A. Welch Foundation, the Hamill Foundation, the National Research Foundation of Korea, and the T.J. Park Science Fellowship of POSCO supported the research. (Figure: Complex molecular interactions among microbial consortia is simplified as interactions among points on a limit cycle (right).)
Efficiently Producing Fatty Acids and Biofuels from Glucose
Researchers have presented a new strategy for efficiently producing fatty acids and biofuels that can transform glucose and oleaginous microorganisms into microbial diesel fuel, with one-step direct fermentative production. The newly developed strain, created by Distinguished Professor Sang Yup Lee and his team, showed the highest efficiency in producing fatty acids and biodiesels ever reported. It will be expected to serve as a new platform to sustainably produce a wide array of fatty acid-based products from glucose and other carbon substrates. Fossil fuels, which have long been energy resources for our daily lives, are now facing serious challenges: depletion of their reserves and their role in global warming. The production of sustainable bio-based renewable energy has emerged as an essential alternative and many studies to replace fossil fuels are underway. One of the representative examples is biodiesel. Currently, it is mainly being produced through the transesterification of vegetable oils or animal fats. The research team engineered oleaginous microorganisms, Rhodococcus opacus, to produce fatty acids and their derivatives that can be used as biodiesel from glucose, one of the most abundant and cheap sugars derived from non-edible biomass. Professor Lee’s team has already engineered Escherichia coli to produce short-chain hydrocarbons, which can be used as gasoline (published in Nature as the cover paper in 2013). However, the production efficiency of the short-chain hydrocarbons using E. coli (0.58 g/L) fell short of the levels required for commercialization. To overcome these issues, the team employed oil-accumulating Rhodococcus opacus as a host strain in this study. First, the team optimized the cultivation conditions of Rhodococcus opacus to maximize the accumulation of oil (triacylglycerol), which serves as a precursor for the biosynthesis of fatty acids and their derivatives. Then, they systematically analyzed the metabolism of the strain and redesigned it to enable higher levels of fatty acids and two kinds of fatty acid-derived biodiesels (fatty acid ethyl esters and long-chain hydrocarbons) to be produced. They found that the resulting strains produced 50.2, 21.3, and 5.2 g/L of fatty acids, fatty acid ethyl esters, and long-chain hydrocarbons, respectively. These are all the highest concentrations ever reported by microbial fermentations. It is expected that these strains can contribute to the future industrialization of microbial-based biodiesel production. “This technology creates fatty acids and biodiesel with high efficiency by utilizing lignocellulose, one of the most abundant resources on the Earth, without depending on fossil fuels and vegetable or animal oils. This will provide new opportunities for oil and petroleum industries, which have long relied on fossil fuels, to turn to sustainable and eco-friendly biotechnologies,” said Professor Lee. This paper titled “Engineering of an oleaginous bacterium for the production of fatty acids and fuels” was published in Nature Chemical Biology on June 17. This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea (NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557). (Figure: Metabolic engineering for the production of free fatty acids (FFAs), fatty acid ethyl esters (FAEEs), and long-chain hydrocarbons (LCHCs) in Rhodococcus opacus PD630. Researchers have presented a new strategy for efficiently producing fatty acids and biofuels that can transform glucose and oleaginous microorganisms into microbial diesel fuel, with one-step direct fermentative production.) # # # Source: Hye Mi Kim, Tong Un Chae, So Young Choi, Won Jun Kim and Sang Yup Lee. Engineering of an oleaginous bacterium for the production of fatty acids and fuels. Nature Chemical Biology ( https://www.nature.com/nchembio/ ) DOI: 10.1038/s41589-019-0295-5 Profile Dr. Sang Yup Lee firstname.lastname@example.org Distinguished Professor at the Department of Chemical and Biomolecular Engineering KAIST
Ligand Recognition Mechanism of Protein Identified
Professor Hak-Sung Kim -“Solved the 50 year old mystery of how protein recognises and binds to ligands” - Exciting potential for understanding life phenomena and the further development of highly effective therapeutic agent development KAIST’s Biological Science Department’s Professor Hak-Sung Kim, working in collaboration with Professor Sung-Chul Hong of Department of Physics, Seoul National University, has identified the mechanism of how the protein recognizes and binds to ligands within the human body. The research findings were published in the online edition of Nature Chemical Biology (March 18), which is the most prestigious journal in the field of life science. Since the research identified the mechanism, of which protein recognises and binds to ligands, it will take an essential role in understanding complex life phenomenon by understanding regulatory function of protein. Also, ligand recognition of proteins is closely related to the cause of various diseases. Therefore the research team hopes to contribute to the development of highly effective treatments. Ligands, well-known examples include nucleic acid and proteins, form the structure of an organism or are essential constituents with special functions such as information signalling. In particular, the most important role of protein is recognising and binding to a particular ligand and hence regulating and maintaining life phenomena. The abnormal occurrence of an error in recognition of ligands may lead to various diseases. The research team focused on the repetition of change in protein structure from the most stable “open form” to a relatively unstable “partially closed form”. Professor Kim’s team analysed the change in protein structure when binding to a ligand on a molecular level in real time to explain the ligand recognition mechanism. The research findings showed that ligands prefer the most stable protein structure. The team was the first in the world to identify that ligands alter protein structure to the most stable, the lowest energy level, when it binds to the protein. In addition, the team found that ligands bind to unstable partially-closed forms to change protein structure. The existing models to explain ligand recognition mechanism of protein are “Induced Custom Model”, which involves change in protein structure in binding to ligands, and the “Structure Selection Model”, which argues that ligands select and recognise only the best protein structure out of many. The academic world considers that the team’s research findings have perfectly proved the models through experiments for the first time in the world. Professor Kim explained, “In the presence of ligands, there exists a phenomenon where the speed of altering protein structure is changed. This phenomenon is analysed on a molecular level to prove ligand recognition mechanism of protein for the first time”. He also said, “The 50-year old mystery, that existed only as a hypothesis on biology textbooks and was thought never to be solved, has been confirmed through experiments for the first time.” Figure 1: Proteins, with open and partially open form, recognising and binding to ligands. Figure 2: Ligands temporarily bind to a stable protein structure, open form, which changes into the most stable structure, closed form. In addition, binding to partially closed form also changes protein structure to closed form.
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