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Du J, Shui H, Chen R, Dong Y, Xiao C, Hu Y, Wong NK. Neuraminidase-1 (NEU1): Biological Roles and Therapeutic Relevance in Human Disease. Curr Issues Mol Biol 2024; 46:8031-8052. [PMID: 39194692 DOI: 10.3390/cimb46080475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/24/2024] [Accepted: 07/24/2024] [Indexed: 08/29/2024] Open
Abstract
Neuraminidases catalyze the desialylation of cell-surface glycoconjugates and play crucial roles in the development and function of tissues and organs. In both physiological and pathophysiological contexts, neuraminidases mediate diverse biological activities via the catalytic hydrolysis of terminal neuraminic, or sialic acid residues in glycolipid and glycoprotein substrates. The selective modulation of neuraminidase activity constitutes a promising strategy for treating a broad spectrum of human pathologies, including sialidosis and galactosialidosis, neurodegenerative disorders, cancer, cardiovascular diseases, diabetes, and pulmonary disorders. Structurally distinct as a large family of mammalian proteins, neuraminidases (NEU1 through NEU4) possess dissimilar yet overlapping profiles of tissue expression, cellular/subcellular localization, and substrate specificity. NEU1 is well characterized for its lysosomal catabolic functions, with ubiquitous and abundant expression across such tissues as the kidney, pancreas, skeletal muscle, liver, lungs, placenta, and brain. NEU1 also exhibits a broad substrate range on the cell surface, where it plays hitherto underappreciated roles in modulating the structure and function of cellular receptors, providing a basis for it to be a potential drug target in various human diseases. This review seeks to summarize the recent progress in the research on NEU1-associated diseases and highlight the mechanistic implications of NEU1 in disease pathogenesis. An improved understanding of NEU1-associated diseases should help accelerate translational initiatives to develop novel or better therapeutics.
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Affiliation(s)
- Jingxia Du
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Hanqi Shui
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Rongjun Chen
- Clinical Pharmacology Section, Department of Pharmacology, Shantou University Medical College, Shantou 515041, China
| | - Yibo Dong
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Chengyao Xiao
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Yue Hu
- College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, China
| | - Nai-Kei Wong
- Clinical Pharmacology Section, Department of Pharmacology, Shantou University Medical College, Shantou 515041, China
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Nguyen TH, Thai QM, Pham MQ, Minh PTH, Phung HTT. Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds. Mol Divers 2024; 28:553-561. [PMID: 36823394 PMCID: PMC9950021 DOI: 10.1007/s11030-023-10601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/04/2023] [Indexed: 02/25/2023]
Abstract
To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.
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Affiliation(s)
- Trung Hai Nguyen
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Quynh Mai Thai
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Minh Quan Pham
- Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Pham Thi Hong Minh
- Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Huong Thi Thu Phung
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
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Puhl AC, Lane TR, Ekins S. Learning from COVID-19: How drug hunters can prepare for the next pandemic. Drug Discov Today 2023; 28:103723. [PMID: 37482237 PMCID: PMC10994687 DOI: 10.1016/j.drudis.2023.103723] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 07/25/2023]
Abstract
Over 3 years, the SARS-CoV-2 pandemic killed nearly 7 million people and infected more than 767 million globally. During this time, our very small company was able to contribute to antiviral drug discovery efforts through global collaborations with other researchers, which enabled the identification and repurposing of multiple molecules with activity against SARS-CoV-2 including pyronaridine tetraphosphate, tilorone, quinacrine, vandetanib, lumefantrine, cetylpyridinium chloride, raloxifene, carvedilol, olmutinib, dacomitinib, crizotinib, and bosutinib. We highlight some of the key findings from this experience of using different computational and experimental strategies, and detail some of the challenges and strategies for how we might better prepare for the next pandemic so that potential antiviral treatments are available for future outbreaks.
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Affiliation(s)
- Ana C Puhl
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA.
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, USA.
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Visibelli A, Roncaglia B, Spiga O, Santucci A. The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines 2023; 11:887. [PMID: 36979866 PMCID: PMC10045927 DOI: 10.3390/biomedicines11030887] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/08/2023] [Indexed: 03/16/2023] Open
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.
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Affiliation(s)
- Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Bianca Roncaglia
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
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Ngo ST, Nguyen TH, Tung NT, Vu VV, Pham MQ, Mai BK. Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches. Phys Chem Chem Phys 2022; 24:29266-29278. [PMID: 36449268 DOI: 10.1039/d2cp04476e] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.
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Affiliation(s)
- Son Tung Ngo
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Trung Hai Nguyen
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Nguyen Thanh Tung
- Institute of Materials Science, Vietnam Academy of Science and Technology, Hanoi, Vietnam. .,Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Van V Vu
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
| | - Minh Quan Pham
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam.,Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Binh Khanh Mai
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
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Urbina F, Ekins S. The Commoditization of AI for Molecule Design. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2022; 2:100031. [PMID: 36211981 PMCID: PMC9541920 DOI: 10.1016/j.ailsci.2022.100031] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will increasingly require machine learning models for design and optimization of molecules as they become "designed by AI". AI and machine learning has essentially become a commodity within the pharmaceutical industry. This perspective will briefly describe our personal opinions of how machine learning has evolved and is being applied to model different molecule properties that crosses industries in their utility and ultimately suggests the potential for tight integration of AI into equipment and automated experimental pipelines. It will also describe how many groups have implemented generative models covering different architectures, for de novo design of molecules. We also highlight some of the companies at the forefront of using AI to demonstrate how machine learning has impacted and influenced our work. Finally, we will peer into the future and suggest some of the areas that represent the most interesting technologies that may shape the future of molecule design, highlighting how we can help increase the efficiency of the design-make-test cycle which is currently a major focus across industries.
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Affiliation(s)
- Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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Urbina F, Lowden CT, Culberson JC, Ekins S. MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction. ACS OMEGA 2022; 7:18699-18713. [PMID: 35694522 PMCID: PMC9178760 DOI: 10.1021/acsomega.2c01404] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/11/2022] [Indexed: 05/04/2023]
Abstract
Generative machine learning models have become widely adopted in drug discovery and other fields to produce new molecules and explore molecular space, with the goal of discovering novel compounds with optimized properties. These generative models are frequently combined with transfer learning or scoring of the physicochemical properties to steer generative design, yet often, they are not capable of addressing a wide variety of potential problems, as well as converge into similar molecular space when combined with a scoring function for the desired properties. In addition, these generated compounds may not be synthetically feasible, reducing their capabilities and limiting their usefulness in real-world scenarios. Here, we introduce a suite of automated tools called MegaSyn representing three components: a new hill-climb algorithm, which makes use of SMILES-based recurrent neural network (RNN) generative models, analog generation software, and retrosynthetic analysis coupled with fragment analysis to score molecules for their synthetic feasibility. We show that by deconstructing the targeted molecules and focusing on substructures, combined with an ensemble of generative models, MegaSyn generally performs well for the specific tasks of generating new scaffolds as well as targeted analogs, which are likely synthesizable and druglike. We now describe the development, benchmarking, and testing of this suite of tools and propose how they might be used to optimize molecules or prioritize promising lead compounds using these RNN examples provided by multiple test case examples.
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Affiliation(s)
- Fabio Urbina
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Christopher T. Lowden
- Workflow
Informatics Corporation, 9316 Bramden Court, Wake Forest, North Carolina 27587, United States
| | - J. Christopher Culberson
- Workflow
Informatics Corporation, 9316 Bramden Court, Wake Forest, North Carolina 27587, United States
| | - Sean Ekins
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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Oyewola DO, Dada EG. Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer. SN APPLIED SCIENCES 2022; 4:143. [PMID: 35434524 PMCID: PMC8996204 DOI: 10.1007/s42452-022-05027-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 11/10/2021] [Indexed: 02/07/2023] Open
Abstract
Machine Learning has found application in solving complex problems in different fields of human endeavors such as intelligent gaming, automated transportation, cyborg technology, environmental protection, enhanced health care, innovation in banking and home security, and smart homes. This research is motivated by the need to explore the global structure of machine learning to ascertain the level of bibliographic coupling, collaboration among research institutions, co-authorship network of countries, and sources coupling in publications on machine learning techniques. The Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to clustering prediction of authors dominance ranking in this paper. Publications related to machine learning were retrieved and extracted from the Dimensions database with no language restrictions. Bibliometrix was employed in computation and visualization to extract bibliographic information and perform a descriptive analysis. VOSviewer (version 1.6.16) tool was used to construct and visualize structure map of source coupling networks of researchers and co-authorship. About 10,814 research papers on machine learning published from 2010 to 2020 were retrieved for the research. Experimental results showed that the highest degree of betweenness centrality was obtained from cluster 3 with 153.86 from the University of California and Harvard University with 24.70. In cluster 1, the national university of Singapore has the highest degree betweenness of 91.72. Also, in cluster 5, the University of Cambridge (52.24) and imperial college London (4.52) having the highest betweenness centrality manifesting that he could control the collaborative relationship and that they possessed and controlled a large number of research resources. Findings revealed that this work has the potential to provide valuable guidance for new perspectives and future research work in the rapidly developing field of machine learning.
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Affiliation(s)
- David Opeoluwa Oyewola
- Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B 0182, Gombe, Nigeria
| | - Emmanuel Gbenga Dada
- Department of Mathematical Sciences, Faculty of Science, University of Maiduguri, Maiduguri, Nigeria
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Phase-In to Phase-Out—Targeted, Inclusive Strategies Are Needed to Enable Full Replacement of Animal Use in the European Union. Animals (Basel) 2022; 12:ani12070863. [PMID: 35405853 PMCID: PMC8997151 DOI: 10.3390/ani12070863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/17/2022] [Accepted: 03/25/2022] [Indexed: 12/04/2022] Open
Abstract
Simple Summary In the European Union (and elsewhere), the overall use of animals in laboratories has failed to undergo any significant decline, despite six decades of purported adherence to the “3Rs” principles of replacement, reduction, and refinement. In the EU, the 1986 adoption of a legal requirement to use scientific methods not entailing the use of live animals, rising public opinion against the use of animals and the almost exponential rise in development and application of non-animal new approach methodologies (NAMs) signals a readiness to end animal testing. Indeed, the European Parliament recently carried an almost unanimous vote to adopt an action plan to phase out the use of animals in research and testing. This article explores what is needed to make this action plan a success, considering all stakeholders and their needs. Abstract In September 2021, the European Parliament voted overwhelmingly in favour of a resolution to phase out animal use for research, testing, and education, through the adoption of an action plan. Here we explore the opportunity that the action plan could offer in developing a more holistic outlook for fundamental and biomedical research, which accounts for around 70% of all animal use for scientific purposes in the EU. We specifically focus on biomedical research to consider how mapping scientific advances to patient needs, taking into account the ambitious health policies of the EU, would facilitate the development of non-animal strategies to deliver safe and effective medicines, for example. We consider what is needed to help accelerate the move away from animal use, taking account of all stakeholders and setting ambitious but realistic targets for the total replacement of animals. Importantly, we envisage this as a ‘phase-in’ approach, encouraging the use of human-relevant NAMs, enabling their development and application across research (with applications for toxicology testing). We make recommendations for three pillars of activity, inspired by similar efforts for making the shift to renewable energy and reducing carbon emissions, and point out where investment—both financial and personnel—may be needed.
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Zou B, Cao C, Fu Y, Pan D, Wang W, Kong L. Berberine Alleviates Gastroesophageal Reflux-Induced Airway Hyperresponsiveness in a Transient Receptor Potential A1-Dependent Manner. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:7464147. [PMID: 35586690 PMCID: PMC9110152 DOI: 10.1155/2022/7464147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 03/24/2022] [Accepted: 04/21/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND To investigate the beneficial effect of berberine on gastroesophageal reflux-induced airway hyperresponsiveness (GERAHR) and explore the underlying mechanism. METHODS Coword cluster analysis and strategic coordinates were used to identify hotspots for GERAHR research, and an online tool (STRING, https://string-db.org/) was used to predict the potential relationships between proteins. Guinea pigs with chemically induced GERAHR received PBS or different berberine-based treatments to evaluate the therapeutic effect of berberine and characterize the underlying mechanism. Airway responsiveness was assessed using a plethysmography system, and protein expression was evaluated by western blotting, immunohistochemical staining, and quantitative PCR analysis. RESULTS Bioinformatics analyses revealed that TRP channels are hotspots of GERAHR research, and TRPA1 is related to the proinflammatory neuropeptide substance P (SP). Berberine, especially at the middle dose tested (MB, 150 mg/kg), significantly improved lung function, suppressed inflammatory cell infiltration, and protected inflammation-driven tissue damage in the lung, trachea, esophagus, and nerve tissues in GERAHR guinea pigs. MB reduced the expression of TRPA1, SP, and tumor necrosis factor-alpha (TNF-α) in evaluated organs and tissues. Meanwhile, the MB-mediated protective effects were attenuated by simultaneous TRPA1 activation. CONCLUSIONS Mechanistically, berberine was found to suppress GERAHR-induced upregulation of TRPA1, SP, and TNF-α in many tissues. Our study has highlighted the potential therapeutic value of berberine for the treatment of GERAHR.
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Affiliation(s)
- Bo Zou
- Institute of Respiratory Diseases, The First Hospital of China Medical University, Shenyang City, Liaoning Province, China
- Department of Respiratory and Critical Care Medicine, The Fourth Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, China
| | - Chaofan Cao
- Institute of Respiratory Diseases, The First Hospital of China Medical University, Shenyang City, Liaoning Province, China
- Department of Respiratory Medicine, The Second Affiliated Hospital of Shenyang Medical College, Shenyang City, Liaoning Province, China
| | - Yue Fu
- Institute of Respiratory Diseases, The First Hospital of China Medical University, Shenyang City, Liaoning Province, China
| | - Dianzhu Pan
- Department of Respiratory Medicine, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Wei Wang
- Institute of Respiratory Diseases, The First Hospital of China Medical University, Shenyang City, Liaoning Province, China
| | - Lingfei Kong
- Institute of Respiratory Diseases, The First Hospital of China Medical University, Shenyang City, Liaoning Province, China
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