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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.07.31.551116v1?rss=1
Authors: Taye, B., Thuenauer, R., Sugrue, R. J., Maurer-Stroh, S., Kosinski, J.
Abstract:
Influenza A virus (IAV) causes up to five million cases of severe illness and half a million deaths worldwide each year. While there are a few clinically approved drugs for treating IAV, they are challenged by the rapid evolution of the virus leading to emergence of drug resistance and the adverse effects of the drugs. Targeting host cellular factors that support virus replication could limit resistance, increase the broad-spectrum antiviral properties of drugs, and benefit from repurposing drugs already existing against those factors. However, selecting the right drug-target pairs with low toxicity and minimal adverse effects has been challenging, even though hundreds of cellular host factors have been identified. In this study, we applied a computational and knowledge-based drug-target prioritization approach to identify promising drug-target pairs. We selected five pairs for experimental validation: telmisartan-Angiotensin II receptor, type 1 (AGTR1), metoclopramide hydrochloride-Cholinergic receptor muscarinic 1 (CHRM1), cefepime hydrochloride-phosphogluconate dehydrogenase (PGD), ranolazine dihydrochloride-sodium channel voltage-gated type v alpha subunit (SCN5A), and ofloxacin-topoisomerase II alpha 170kDa (TOP2A). Except for cefepime hydrochloride, all four drugs showed significant plaque reduction in Madin Darby canine kidney (MDCK) cells. In the immunofluorescence assay, metoclopramide hydrochloride, ranolazine dihydrochloride, and telmisartan showed antiviral activity in MDCK and/or adenocarcinoma human alveolar basal epithelial (A549) cell lines. In conclusion, our approach can prioritize and identify drugs with antiviral activity against influenza virus. Refining and strengthening such approaches could be valuable for rapid antiviral discovery and pandemic preparedness.
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http://biorxiv.org/cgi/content/short/2023.07.31.551116v1?rss=1
Authors: Taye, B., Thuenauer, R., Sugrue, R. J., Maurer-Stroh, S., Kosinski, J.
Abstract:
Influenza A virus (IAV) causes up to five million cases of severe illness and half a million deaths worldwide each year. While there are a few clinically approved drugs for treating IAV, they are challenged by the rapid evolution of the virus leading to emergence of drug resistance and the adverse effects of the drugs. Targeting host cellular factors that support virus replication could limit resistance, increase the broad-spectrum antiviral properties of drugs, and benefit from repurposing drugs already existing against those factors. However, selecting the right drug-target pairs with low toxicity and minimal adverse effects has been challenging, even though hundreds of cellular host factors have been identified. In this study, we applied a computational and knowledge-based drug-target prioritization approach to identify promising drug-target pairs. We selected five pairs for experimental validation: telmisartan-Angiotensin II receptor, type 1 (AGTR1), metoclopramide hydrochloride-Cholinergic receptor muscarinic 1 (CHRM1), cefepime hydrochloride-phosphogluconate dehydrogenase (PGD), ranolazine dihydrochloride-sodium channel voltage-gated type v alpha subunit (SCN5A), and ofloxacin-topoisomerase II alpha 170kDa (TOP2A). Except for cefepime hydrochloride, all four drugs showed significant plaque reduction in Madin Darby canine kidney (MDCK) cells. In the immunofluorescence assay, metoclopramide hydrochloride, ranolazine dihydrochloride, and telmisartan showed antiviral activity in MDCK and/or adenocarcinoma human alveolar basal epithelial (A549) cell lines. In conclusion, our approach can prioritize and identify drugs with antiviral activity against influenza virus. Refining and strengthening such approaches could be valuable for rapid antiviral discovery and pandemic preparedness.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.07.31.551263v1?rss=1
Authors: Doria-Belenguer, S., Xenos, A., Ceddia, G., Malod-Dognin, N., Przulj, N.
Abstract:
Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly…
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.01.551433v1?rss=1
Authors: Rrustemi, T., Meyer, K., Roske, Y., Uyar, B., Akalin, A., Imami, K., Ishihama, Y., Daumke, O., Selbach, M.
Abstract:
Despite their lack of a defined 3D structure, intrinsically disordered regions (IDR…
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.03.550998v1?rss=1
Authors: Graves, J. L., McKenzie, B., Koch, Z., Naka, A. S., Spofford, N., Morrison, J.
Abstract:
OBJECTIVE To evaluate age, sex, body weight, breed, neuter status, and age at neutering as risk factors for dia…
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.07.31.551408v1?rss=1
Authors: Gupta, R., Adhikary, S., Dalpatraj, N., Laxman, S.
Abstract:
Cells require disparate amounts of distinct amino acids, which themselves have discrete biosynthetic costs. However, it remains unclear if …
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