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Universal Virus Detection Platform to Expedite Viral Diagnosis
Reactive polymer-based tester pre-screens dsRNAs of a wide range of viruses without their genome sequences The prompt, precise, and massive detection of a virus is the key to combat infectious diseases such as Covid-19. A new viral diagnostic strategy using reactive polymer-grafted, double-stranded RNAs will serve as a pre-screening tester for a wide range of viruses with enhanced sensitivity. Currently, the most widely using viral detection methodology is polymerase chain reaction (PCR) diagnosis, which amplifies and detects a piece of the viral genome. Prior knowledge of the relevant primer nucleic acids of the virus is quintessential for this test. The detection platform developed by KAIST researchers identifies viral activities without amplifying specific nucleic acid targets. The research team, co-led by Professor Sheng Li and Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering, constructed a universal virus detection platform by utilizing the distinct features of the PPFPA-grafted surface and double-stranded RNAs. The key principle of this platform is utilizing the distinct feature of reactive polymer-grafted surfaces, which serve as a versatile platform for the immobilization of functional molecules. These activated surfaces can be used in a wide range of applications including separation, delivery, and detection. As long double-stranded RNAs are common byproducts of viral transcription and replication, these PPFPA-grafted surfaces can detect the presence of different kinds of viruses without prior knowledge of their genomic sequences. “We employed the PPFPA-grafted silicon surface to develop a universal virus detection platform by immobilizing antibodies that recognize double-stranded RNAs,” said Professor Kim. To increase detection sensitivity, the research team devised two-step detection process analogues to sandwich enzyme-linked immunosorbent assay where the bound double-stranded RNAs are then visualized using fluorophore-tagged antibodies that also recognize the RNAs’ double-stranded secondary structure. By utilizing the developed platform, long double-stranded RNAs can be detected and visualized from an RNA mixture as well as from total cell lysates, which contain a mixture of various abundant contaminants such as DNAs and proteins. The research team successfully detected elevated levels of hepatitis C and A viruses with this tool. “This new technology allows us to take on virus detection from a new perspective. By targeting a common biomarker, viral double-stranded RNAs, we can develop a pre-screening platform that can quickly differentiate infected populations from non-infected ones,” said Professor Li. “This detection platform provides new perspectives for diagnosing infectious diseases. This will provide fast and accurate diagnoses for an infected population and prevent the influx of massive outbreaks,” said Professor Kim. This work is featured in Biomacromolecules. This work was supported by the Agency for Defense Development (Grant UD170039ID), the Ministry of Science and ICT (NRF-2017R1D1A1B03034660, NRF-2019R1C1C1006672), and the KAIST Future Systems Healthcare Project from the Ministry of Science and ICT (KAISTHEALTHCARE42). Profile:-Professor Yoosik KimDepartment of Chemical and Biomolecular Engineeringhttps://qcbio.kaist.ac.kr KAIST-Professor Sheng LiDepartment of Chemical and Biomolecular Engineeringhttps://bcpolymer.kaist.ac.kr KAIST Publication:Ku et al., 2020. Reactive Polymer Targeting dsRNA as Universal Virus Detection Platform with Enhanced Sensitivity. Biomacromolecules (https://doi.org/10.1021/acs.biomac.0c00379).
What Fuels a “Domino Effect” in Cancer Drug Resistance?
KAIST researchers have identified mechanisms that relay prior acquired resistance to the first-line chemotherapy to the second-line targeted therapy, fueling a “domino effect” in cancer drug resistance. Their study featured in the February 7 edition of Science Advances suggests a new strategy for improving the second-line setting of cancer treatment for patients who showed resistance to anti-cancer drugs. Resistance to cancer drugs is often managed in the clinic by chemotherapy and targeted therapy. Unlike chemotherapy that works by repressing fast-proliferating cells, targeted therapy blocks a single oncogenic pathway to halt tumor growth. In many cases, targeted therapy is engaged as a maintenance therapy or employed in the second-line after front-line chemotherapy. A team of researchers led by Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering and the KAIST Institute for Health Science and Technology (KIHST) has discovered an unexpected resistance signature that occurs between chemotherapy and targeted therapy. The team further identified a set of integrated mechanisms that promotes this kind of sequential therapy resistance. “There have been multiple clinical accounts reflecting that targeted therapies tend to be least successful in patients who have exhausted all standard treatments,” said the first author of the paper Mark Borris D. Aldonza. He continued, “These accounts ignited our hypothesis that failed responses to some chemotherapies might speed up the evolution of resistance to other drugs, particularly those with specific targets.” Aldonza and his colleagues extracted large amounts of drug-resistance information from the open-source database the Genomics of Drug Sensitivity in Cancer (GDSC), which contains thousands of drug response data entries from various human cancer cell lines. Their big data analysis revealed that cancer cell lines resistant to chemotherapies classified as anti-mitotic drugs (AMDs), toxins that inhibit overacting cell division, are also resistant to a class of targeted therapies called epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). In all of the cancer types analyzed, more than 84 percent of those resistant to AMDs, representatively ‘paclitaxel’, were also resistant to at least nine EGFR-TKIs. In lung, pancreatic, and breast cancers where paclitaxel is often used as a first-line, standard-of-care regimen, greater than 92 percent showed resistance to EGFR-TKIs. Professor Kim said, “It is surprising to see that such collateral resistance can occur specifically between two chemically different classes of drugs.” To figure out how failed responses to paclitaxel leads to resistance to EGFR-TKIs, the team validated co-resistance signatures that they found in the database by generating and analyzing a subset of slow-doubling, paclitaxel-resistant cancer models called ‘persisters’. The results demonstrated that paclitaxel-resistant cancers remodel their stress response by first becoming more stem cell-like, evolving the ability to self-renew to adapt to more stressful conditions like drug exposures. More surprisingly, when the researchers characterized the metabolic state of the cells, EGFR-TKI persisters derived from paclitaxel-resistant cancer cells showed high dependencies to energy-producing processes such as glycolysis and glutaminolysis. “We found that, without an energy stimulus like glucose, these cells transform to becoming more senescent, a characteristic of cells that have arrested cell division. However, this senescence is controlled by stem cell factors, which the paclitaxel-resistant cancers use to escape from this arrested state given a favorable condition to re-grow,” said Aldonza. Professor Kim explained, “Before this research, there was no reason to expect that acquiring the cancer stem cell phenotype that dramatically leads to a cascade of changes in cellular states affecting metabolism and cell death is linked with drug-specific sequential resistance between two classes of therapies.” He added, “The expansion of our work to other working models of drug resistance in a much more clinically-relevant setting, perhaps in clinical trials, will take on increasing importance, as sequential treatment strategies will continue to be adapted to various forms of anti-cancer therapy regimens.” This study was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF-2016R1C1B2009886), and the KAIST Future Systems Healthcare Project (KAISTHEALTHCARE42) funded by the Korean Ministry of Science and ICT (MSIT). Undergraduate student Aldonza participated in this research project and presented the findings as the lead author as part of the Undergraduate Research Participation (URP) Program at KAIST. < Figure 1. Schematic overview of the study. > < Figure 2. Big data analysis revealing co-resistance signatures between classes of anti-cancer drugs. > Publication: Aldonza et al. (2020) Prior acquired resistance to paclitaxel relays diverse EGFR-targeted therapy persistence mechanisms. Science Advances, Vol. 6, No. 6, eaav7416. Available online at http://dx.doi.org/10.1126/sciadv.aav7416 Profile: Prof. Yoosik Kim, MA, PhD firstname.lastname@example.org https://qcbio.kaist.ac.kr/ Assistant Professor Bio Network Analysis Laboratory Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon, Republic of Korea Profile: Mark Borris D. Aldonza email@example.com Undergraduate Student Department of Biological Sciences Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon, Republic of Korea (END)
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