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Experimental modeling of Alzheimer's disease: Translational lessons from cross-taxon analyses

Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第5期21卷 e70273页

作者： Konstantin B Yenkoyan Maria M Kotova Kirill V Apukhtin David S Galstyan Tamara G Amstislavskaya Tatyana Strekalova Murilo S de Abreu Vergine A Chavushyan Lee Wei Lim Longen Yang Denis D Rosemberg Allan V Kalueff Neuroscience Laboratory COBRAIN Center Yerevan State Medical University after M. Heratsi Yerevan Armenia. Neuroscience Program Sirius University of Science and Technology Sochi Russia. Institute of Translational Biomedicine St. Petersburg State University St. Petersburg Russia. Institute of Experimental Medicine Almazov National Medical Research Centre Ministry of Healthcare of Russian Federation St. Petersburg Russia. Scientific Research Institute of Neurosciences and Medicine Novosibirsk Russia. Research Institute of General Pathology and Pathophysiology Moscow Russia. Graduate Program in Health Sciences Federal University of Health Sciences of Porto Alegre Porto Alegre Brazil. Department of Biosciences and Bioinformatics School of Science Xi'an Jiaotong-Liverpool University Suzhou China. Suzhou Municipal Key Laboratory of Neurobiology and Cell Signaling School of Science Xi'an Jiaotong-Liverpool University Suzhou China. Laboratory of Experimental Neuropsychobiology Department of Biochemistry and Molecular Biology Natural and Exact Sciences Center Federal University of Santa Maria Santa Maria Brazil. Graduate Program in Biological Sciences: Toxicological Biochemistry Federal University of Santa Maria Santa Maria Brazil. The International Zebrafish Neuroscience Research Consortium (ZNRC) Slidell Louisiana USA.

Alzheimer's disease (AD) is a severely debilitating neurodegenerative disease with a rapidly increasing global prevalence, poorly understood causes, and no efficient treatments. Experimental models are valuable for studying AD pathogenesis, including amyloid beta and tau accumulation, synaptic dysfunction, and neuroinflammation. While no model fully reproduces the disease, we take an evolutionary biology approach to discuss available models across taxa, from mammals (rodents, primates) to zebrafish, Drosophila melanogaster, and Caenorhabditis elegans. Evaluating their strengths and limitations provides insight into disease mechanisms and may refine research strategies for improved diagnostics and therapeutic screening. Traditional models have significantly contributed to AD research, yet their translational limitations highlight the need for physiologically relevant alternatives. Integrating humanized rodent models, zebrafish, organoids, and induced pluripotent stem cell-based systems-along with advances in bioengineering and genetic editing-may offer a more comprehensive framework to bridge the gap between preclinical research and clinical application. HIGHLIGHTS: Experimental models across rodents, primates, zebrafish, fruit flies, and worms provide key insights into Alzheimer's disease (AD). Cross-taxon comparisons assess strengths and weaknesses in AD models. Evolutionary biology approaches refine experimental strategies for AD research. Diverse animal models improve understanding of AD pathogenesis. Cross-species models enhance diagnostics and therapeutic strategy development.

关键词： Alzheimer's disease animal models cross‐taxon analyses evolutionary psychiatry translation medicine

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Inhibition of topoisomerase IIα and induction of DNA damage in cholangiocarcinoma cells by altholactone and its halogenated benzoate derivatives

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Biomedicine and Pharmacotherapy 2020年 127卷 110149-110149页

作者： Kitdumrongthum, Sarunya Reabroi, Somrudee Suksen, Kanoknetr Tuchinda, Patoomratana Munyoo, Bamroong Mahalapbutr, Panupong Rungrotmongkol, Thanyada Ounjai, Puey Chairoungdua, Arthit Toxicology Graduate Program Faculty of Science Mahidol University Bangkok Thailand Excellent Center for Drug Discovery (ECDD) Mahidol University Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Department of Physiology Faculty of Science Mahidol University Bangkok Thailand Department of Chemistry Faculty of Science Mahidol University Bangkok Thailand Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Department of Biology Faculty of Science Mahidol University Bangkok Thailand

Topoisomerase IIα enzyme (Topo IIα) plays a critical function in DNA replication process and is considered to be a promising target of anti-cancer drugs. In the present study, we reported that the altholactone derivatives modified by adding a halogenated benzoate group showed greater inhibitory activity on Topo IIα enzyme in cell-free system concomitant with cytotoxicity against the CCA cell lines (KKU-M055 and KKU-M213) than those of the parent altholactone. However, the cytotoxic activities of four halogenated benzoate altholactone derivatives including iodo-, fluoro-, chloro-, and bromobenzoate derivatives (compound 1, 2, 3, and 4, respectively) were of equal potency. The fluorobenzoate derivative (compound 2) was chosen for investigating the underlying mechanism in CCA cells. Compound 2 arrested CCA cell cycle at sub G1 phase and induced apoptotic cell death. It markedly inhibited Topo IIα protein expression in both KKU-M055 and KKU-M213 cells, which was accompanied by DNA double-strand breaks demonstrated by an increase in phosphorylated H2A.X protein. Interestingly, KKU-M055 cells, which express higher Topo IIα mRNA compared to KKU-M213 cells, showed greater sensitivity to the compound, indicating the selectivity of the compound to Topo IIα enzyme. By computational docking analysis, the binding affinity of altholactone (-52.5 kcal/mol) and compound 2 (-56.7 kcal/mol) were similar to that of the Topo II poison salvicine (-53.7 kcal/mol). The aromatic moiety of both altholactones embedded in a hydrophobic pocket of Topo II ATPase domain. In addition, compound 2 induced the formation of linear DNA in Topo II-mediated cleavage assay. Collectively, our results demonstrate that the addition of fluorobenzoyl group to altholactone enhances potency and selectivity to inhibit type IIα topoisomerases. Atholactone and fluorobenzoate derivative act as Topo II cleavage complexes stabilizing compounds or Topo II poisons preferentially through binding at ATPase domain of Topo

关键词： Altholactone Cholangiocarcinoma Halogenated benzoate Topoisomerase IIα

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RETRACTION: Phosphor-IWS1-dependent U2AF2 splicing regulates trafficking of CAR-E-positive intronless gene mRNAs and sensitivity to viral infection (Retraction of Vol 4, art no 1179, 2021)

COMMUNICATIONS BIOLOGY

引用

COMMUNICATIONS BIOLOGY 2021年第1期4卷 1页

作者： Laliotis, Georgios I. Kenney, Adam D. Chavdoula, Evangelia Orlacchio, Arturo Kaba, Abdul La Ferlita, Alessandro Anastas, Vollter Tsatsanis, Christos Beane, Joal D. Sehgal, Lalit Coppola, Vincenzo Yount, Jacob S. Tsichlis, Philip N. Department of Cancer Biology and Genetics The Ohio State University College of Medicine Columbus Columbus OH 43210 USA The Ohio State University Comprehensive Cancer Center Columbus OH 43210 USA University of Crete School of Medicine Heraklion Crete 71500 Greece Department of Clinical and Experimental Medicine Bioinformatics Unit University of Catania Catania 95131 Italy Tufts Graduate School of Biomedical Sciences Program in Genetics Boston MA 02111 USA Department of Microbial Infection and Immunity and Infectious Diseases Institute The Ohio State University Columbus OH 43210 USA Department of Surgery Division of Surgical Oncology Columbus OH 43210 USA Department of Medicine Division of Hematology The Ohio State University Columbus OH 43210 USA Institute of Molecular Biology and Biotechnology Heraklion Crete 70013 Greece

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Maternal human papillomavirus infection and the risk of congenital malformations: A nationwide population-based cohort study

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Journal of Medical Virology 2024年第4期96卷 e29549-e29549页

作者： Hsieh, Tina Yi Jin Chen, Thomas Yen Ting Liao, Pei-Lun Huang, Jing-Yang Ma, Kevin Sheng-Kai Hung, Yao-Min Chang, Renin Wei, James Cheng-Chung Department of Obstetrics and Gynecology Beth Israel Deaconess Medical Center Boston MA United States Department of Bioinformatics Harvard Medical School Boston MA United States Department of Otolaryngology-Head and Neck Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Department of Dermatology Massachusetts General Hospital Boston MA United States Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taipei Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Recreation and Sports Management Tajen University Pingtung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan

Previous research has explored theories regarding the vertical transmission of human papillomavirus (HPV) infection and its association with adverse pregnancy and perinatal outcomes. However, the impact of maternal HPV infection on congenital anomalies (CAs) in offspring remains relatively understudied. We conducted a population-based cohort study linking the Taiwan Birth Registry, Taiwan Death Registry, and National Health Insurance Research Database, in which newborns born in Taiwan between 2009 and 2015 were included. We established a maternal HPV infection cohort comprising 37 807 newborns and matched them with a comparison group of 151 228 newborns at a 1:4 ratio based on index year, age, and sex. The study examined a composite outcome and subgroups of different types of congenital malformations. Differences in cumulative incidence of CAs were assessed using Kaplan–Meier curves and log-rank tests. Adjusted hazard ratios (aHRs) were estimated using Cox proportional hazard regressions. No significant association was found between HPV infection and the broad spectrum of CAs (aHR: 1.04, 95% confidence interval [CI]: 0.98–1.10;log-rank test p = 0.14). However, we observed a 19% increased risk of musculoskeletal CAs in the maternal HPV infection group (aHR: 1.19;95% CI: 1.05–1.34) compared to those without maternal HPV exposure. Other factors, including the type of HPV (aHR: 0.65;95% CI: 0.16–2.63), the timing of exposure (during or before pregnancy), and maternal age (aHR for <30 years: 1.02, 95% CI: 0.94–1.1;aHR for 30–39 years: 1.05, 95% CI: 0.99–1.11;aHR for ≥40 years: 0.88, 95% CI: 0.67–1.17), did not significantly affect the risk for any CA. In conclusion, gestation detection of HPV infection was associated with musculoskeletal CAs but not other major CAs. Prospective studies are warranted to elucidate the necessity of prenatal screening in populations at risk. © 2024 Wiley Periodicals LLC.

关键词： adverse pregnancy congenital malformations human papillomavirus infection outcomes

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Analyzing important factors in e-commerce for sustainable industry

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Yulius Lie Kartika Purwandari Bharuno Mahesworo Reza Rahutomo Anzaludin Samsinga Perbangsa Hery Harjono Muljo Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The rapid growth of e-commerce for sustainable industry in Indonesia leads to competition for customer acquisition. Instead of gaining loyalty, customers are trapped in a price war and take advantage to compare and choose the one with the best offer. This study aims to analyze important factors of an online retailer to determine customer loyalty in the productive age group. This study contributes to design a questionnaire that can be utilized in analyzing customer loyalty to a retailer in e-commerce (popularly known as the term e-tailer). The questionnaire can be distributed through the Internet to gather demography, population, and e-loyalty factors. Questionnaire design is proposed containing three parts: Part A – Demography, Part B – Targeted Population, and Part C - e-Loyalty Factors. While Part A records basic information and Part B validates, Part C records respondent's assessment to an e - tailer. Therefore, the outcome delivers a data engineering method to help e-tailers determine important factors to gain customer loyalty.

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Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

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Procedia Computer Science 2019年 157卷 31-37页

作者： Puspita Anggraini Kaban Robert Kurniawan Rezzy Eko Caraka Bens Pardamean Budi Yuniarto Sukim Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia College of Informatics Chaoyang University of Technology Taichung City 41349 Taiwan (R.O.C.) Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.

关键词： biclustering disaster mitigation social vulnerability SoVI spatial pattern

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Publisher Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第5期55卷 893页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Cambridge University Hospitals NHS Foundation Trust Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@***. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. hliang1@***. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson

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A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Taufik Budhi Pramono R Ardharyan Islamy Saprudin Joni Johanda Putra Teddy Suparyanto Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Aquaculture Study Program Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Aquatic Resources Management Brawijaya University Malang East Java Indonesia Graduate Program Aquatic Resources Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.

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COVID-19 Testing Pipeline: Lesson Learned

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Dian Amirulloh Digdo Sudigyo Arif Budiarto Ika Nurlaila Erlin Listiyaningsih Andrew Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia 12940 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information System Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) in Indonesia is seen to be uncontrollably increasing that urges the government to leverage the capacity for the disease detections. Real-time polymerase chain reaction (RT-PCR), rapid test and computed tomography (CT) scan are the most common methods to determine if one has been infected regardless of whether or not the common symptoms of such Corona Virus Disease 2019 (COVID-19) surface. Among these three, RT-PCR is considered the gold standard for qualitative and quantitative assessment of SARS CoV-2 detection. The present paper aims at elaborating the framework of Roche's RT-PCR machine employed specifically for SARS CoV-2 detection performed by Genetics Indonesia which is deemed to be efficient and relatively quicker than other detection kits. RT-PCR machine detected SARS Cov-2 with RNA amplification curve equals to 10 copies RNA below the cut off value of Crossing point (Cp) positive control. Also elucidated in the paper is the implementations of EAV RNA and LightCycler® 96 RT-PCR System through which analysis time, amounts of individual required sample, as well as the reagents, can be accordingly reduced.

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