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Wen Y, Yi F, Zhang J, Wang Y, Zhao C, Zhao B, Wang J. Uncovering the protective mechanism of baicalin in treatment of fatty liver based on network pharmacology and cell model of NAFLD. Int Immunopharmacol 2024; 141:112954. [PMID: 39153306 DOI: 10.1016/j.intimp.2024.112954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/25/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Excessive nonesterified fatty acids (NEFA) impair cellular metabolism and will induce fatty liver formation in dairy cows during the periparturient. Baicalin, an active flavonoid, has great potential efficacy in alleviating lipid accumulation and ameliorating the development of fatty liver disease. Nevertheless, its mechanism remains unclear. Here, the potential mechanism of baicalin on system levels was explored using network pharmacology and in vitro experiments. Firstly, the target of baicalin and fatty liver disease was predicted, and then the protein-protein interaction (PPI) network was constructed. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) (q-value) pathway enrichment is performed through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server. Finally, the results of the network analysis of the in vitro treatment of bovine hepatocytes by NEFA were confirmed. The results showed that 33 relevant targets of baicalin in the treatment of liver fatty were predicted by network pharmacology, and the top 20 relevant pathways were extracted by KEGG database. Baicalin treatment can reduce triglyceride (TAG) content and lipid droplet accumulation in NEFA-treated bovine hepatocytes, and the mechanism is related to inhibiting lipid synthesis and promoting lipid oxidation. The alleviating effect of baicalin on fatty liver may be related to the up-regulation of solute vector family member 4 (SLC2A4), Down-regulated AKT serine/threonine kinase 1 (AKT1), Peroxisome proliferator-activated receptor gamma (PPARG), Epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), Interleukin 6 (IL-6) were associated. These results suggested that baicalin may modulate key inflammatory markers, and lipogenesis processes to prevent fatty liver development in dairy cows.
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Affiliation(s)
- Yongqiang Wen
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Fanxuan Yi
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Jia Zhang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Yazhou Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Chenxu Zhao
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Baoyu Zhao
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Jianguo Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China.
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Huang J, Shi R, Chen F, Tan HY, Zheng J, Wang N, Li R, Wang Y, Yang T, Feng Y, Zhong Z. Exploring the anti-hepatocellular carcinoma effects of Xianglian Pill: Integrating network pharmacology and RNA sequencing via in silico and in vitro studies. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 133:155905. [PMID: 39128301 DOI: 10.1016/j.phymed.2024.155905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 05/21/2024] [Accepted: 07/20/2024] [Indexed: 08/13/2024]
Abstract
BACKGROUND Liver cancer represents a most common and fatal cancer worldwide. Xianglian Pill (XLP) is an herbal formula holding great promise in clearing heat for treating diseases in an integrative and holistic way. However, due to the complex constituents and multiple targets, the exact molecular mechanisms of action of XLP are still unclear. PURPOSE This study is focused on hepatocellular carcinoma (HCC), the most common type of liver cancer. The aim of this study is to develop a fast and efficient model to investigate the anti-HCC effects of XLP, and its underlying mechanisms. MATERIALS AND METHODS HepG2, Hep3B, Mahlavu, HuH-7, or Li-7 cells were employed in the studies. The ingredients were analyzed using liquid chromatography tandem mass spectrometry (LC-MS). RNA sequencing combined with network pharmacology was used to elucidate the therapeutic mechanism of XLP in HCC via in silico and in vitro studies. An approach was constructed to improve the accuracy of prediction in network pharmacology by combining big data and omics. RESULTS First, we identified 13 potential ingredients in the serum of XLP-administered rats using LC-MS. Then the network pharmacology was performed to predict that XLP demonstrates anti-HCC effects via targeting 94 genes involving in 13 components. Modifying the database thresholds might impact the accuracy of network pharmacology analysis based on RNA sequencing data. For instance, when the matching rate peak is 0.43, the correctness rate peak is 0.85. Moreover, 9 components of XLP and 6 relevant genes have been verified with CCK-8 and RT-qPCR assay, respectively. CONCLUSION Based on the crossing studies of RNA sequencing and network pharmacology, XLP was found to improve HCC through multiple targets and pathways. Additionally, the study provides a way to optimize network pharmacology analysis in herbal medicine research.
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Affiliation(s)
- Jihan Huang
- Center for Drug Clinical Research, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Ruipeng Shi
- Macao Centre for Research and Development in Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR 999078, China
| | - Feiyu Chen
- School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Hor Yue Tan
- Centre for Chinese Herbal Medicine Drug Development, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Jinbin Zheng
- Macao Centre for Research and Development in Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR 999078, China
| | - Ning Wang
- School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Ran Li
- Center for Drug Clinical Research, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Yulin Wang
- Center for Drug Clinical Research, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Tao Yang
- Center for Drug Clinical Research, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Yibin Feng
- School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.
| | - Zhangfeng Zhong
- Macao Centre for Research and Development in Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR 999078, China.
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Li L, Xiang Y, Hao J. Biomedical event causal relation extraction with deep knowledge fusion and Roberta-based data augmentation. Methods 2024; 231:8-14. [PMID: 39241919 DOI: 10.1016/j.ymeth.2024.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 08/27/2024] [Indexed: 09/09/2024] Open
Abstract
Biomedical event causal relation extraction (BECRE), as a subtask of biomedical information extraction, aims to extract event causal relation facts from unstructured biomedical texts and plays an essential role in many downstream tasks. The existing works have two main problems: i) Only shallow features are limited in helping the model establish potential relationships between biomedical events. ii) Using the traditional oversampling method to solve the data imbalance problem of the BECRE tasks ignores the requirements for data diversifying. This paper proposes a novel biomedical event causal relation extraction method to solve the above problems using deep knowledge fusion and Roberta-based data augmentation. To address the first problem, we fuse deep knowledge, including structural event representation and entity relation path, for establishing potential semantic connections between biomedical events. We use the Graph Convolutional Neural network (GCN) and the predicated tensor model to acquire structural event representation, and entity relation paths are encoded based on the external knowledge bases (GTD, CDR, CHR, GDA and UMLS). We introduce the triplet attention mechanism to fuse structural event representation and entity relation path information. Besides, this paper proposes the Roberta-based data augmentation method to address the second problem, some words of biomedical text, except biomedical events, are masked proportionally and randomly, and then pre-trained Roberta generates data instances for the imbalance BECRE dataset. Extensive experimental results on Hahn-Powell's and BioCause datasets confirm that the proposed method achieves state-of-the-art performance compared to current advances.
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Affiliation(s)
- Lishuang Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Yi Xiang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jing Hao
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
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Wang Z, Huo M, Qiao L, Qiao Y, Zhang Y. SYSTCM: A systemic web platform for objective identification of pharmacological effects based on interplay of "traditional Chinese Medicine-components-targets". Comput Biol Med 2024; 179:108878. [PMID: 39043107 DOI: 10.1016/j.compbiomed.2024.108878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 06/28/2024] [Accepted: 07/10/2024] [Indexed: 07/25/2024]
Abstract
Mechanism analysis is essential for the use and promotion of Traditional Chinese Medicine (TCM). Traditional methods of network analysis relying on expert experience lack an explanatory framework, prompting the application of deep learning and machine learning for objective identification of TCM pharmacological effects. A dataset was used to construct an interacted network graph between 424 molecular descriptors and 465 pharmacological targets to represent the relationship between components and pharmacological effects. Subsequently, the optimal identification model of pharmacological effects (IPE) was established through convolution neural networks of GoogLeNet structure. The AUC values are greater than 0.8, MCC values are greater than 0.7, and ACC values are greater than 0.85 across various test datasets. Subsequently, 18 recognition models of TCM efficacy (RTE) were created using support vector machines (SVM). Integration of pharmacological effects and efficacies led to the development of the systemic web platform for identification of pharmacological effects (SYSTCM). The platform, comprising 70,961 terms, including 636 Traditional Chinese Medicines (TCMs), 8190 components, 40 pharmacological effects, and 18 efficacies. Through the SYSTCM platform, (1) Total 100 components were predicted from TCMs with anti-inflammatory pharmacological effects. (2) The pharmacological effects of complete constituents were predicted from Coptidis Rhizoma (Huang Lian). (3) The principal components, pharmacological effects, and efficacies were elucidated from Salviae Miltiorrhizae radix et rhizome (Dan Shen). SYSTCM addresses subjectivity in pharmacological effect determination, offering a potential avenue for advancing TCM drug development and clinical applications. Access SYSTCM at http://systcm.cn.
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Affiliation(s)
- Zewen Wang
- Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Mengqi Huo
- Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Liansheng Qiao
- Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Yanjiang Qiao
- Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
| | - Yanling Zhang
- Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
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Lu X, Wang T, Hou B, Han N, Li H, Wang X, Xin J, He Y, Zhang D, Jia Z, Wei C. Shensong yangxin, a multi-functional traditional Chinese medicine for arrhythmia: A review of components, pharmacological mechanisms, and clinical applications. Heliyon 2024; 10:e35560. [PMID: 39224243 PMCID: PMC11367280 DOI: 10.1016/j.heliyon.2024.e35560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 07/28/2024] [Accepted: 07/31/2024] [Indexed: 09/04/2024] Open
Abstract
As a common cardiovascular disease (CVD), Arrhythmia refers to any abnormality in the origin, frequency, rhythm, conduction velocity, timing, pathway, sequence, or other aspect of cardiac impulses, and it is one of the common cardiovascular diseases in clinical practice. At present, various ion channel blockers are used for treatment of arrhythmia that include Na+ ion channel blockers, K+ ion channel blockers and Ca2+ ion channel blockers. While these drugs offer benefits, they have led to a gradual increase in drug-related adverse reactions across various systems. As a result, the quest for safe and effective antiarrhythmic drugs is pressing. Recent years have seen some advancements in the treatment of ventricular arrhythmias using traditional Chinese medicine(TCM). The theory of Luobing in TCM has proposed a new drug intervention strategy of "fast and slow treatment, integrated regulation" leading to a shift in mindset from "antiarrhythmic" to "rhythm-regulating". Guided by this theory, the development of Shen Song Yang Xin Capsules (SSYX) has involved various Chinese medicinal ingredients that comprehensively regulate the myocardial electrophysiological mechanism, exerting antiarrhythmic effects on multiple ion channels and non-ion channels. Similarly, in clinical studies, evidence-based research has confirmed that SSYX combined with conventional antiarrhythmic drugs can more effectively reduce the occurrence of arrhythmias. Therefore, this article provides a comprehensive review of the composition and mechanisms of action, pharmacological components, network pharmacology analysis, and clinical applications of SSYX guided by the theory of Luobing, aiming to offer valuable insights for improved clinical management of arrhythmias and related research.
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Affiliation(s)
- Xuan Lu
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School of Hebei Medical University, 050017, China
| | - Tongxing Wang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Bin Hou
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Ningxin Han
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School of Hebei Medical University, 050017, China
| | - Hongrong Li
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Xiaoqi Wang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050090, Hebei, China
| | - Jingjing Xin
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School of Hebei Medical University, 050017, China
| | - Yanling He
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050090, Hebei, China
| | - Dan Zhang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050090, Hebei, China
| | - Zhenhua Jia
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Shijiazhuang Compound Traditional Chinese Medicine Technology Innovation Center, Shijiazhuang, 050035, China
| | - Cong Wei
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Hebei Provincial Key Laboratory of Luobing, Shijiazhuang, 050035, China
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Dutta A, Hossain MA, Somadder PD, Moli MA, Ahmed K, Rahman MM, Bui FM. Exploring the therapeutic targets of stevioside in management of type 2 diabetes by network pharmacology and in-silico approach. Diabetes Metab Syndr 2024; 18:103111. [PMID: 39217825 DOI: 10.1016/j.dsx.2024.103111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 07/17/2024] [Accepted: 08/24/2024] [Indexed: 09/04/2024]
Abstract
AIMS The main objective of the current study is to investigate the pathways and therapeutic targets linked to stevioside in the management of T2D using computational approaches. METHODS We collected RNA-seq datasets from NCBI, then employed GREIN to retrieve differentially expressed genes (DEGs). Computer-assisted techniques DAVID, STRING and NetworkAnalyst were used to explore common significant pathways and therapeutic targets associated with T2D and stevioside. Molecular docking and dynamics simulations were conducted to validate the interaction between stevioside and therapeutic targets. RESULTS Gene ontology and KEGG analysis revealed that prostaglandin synthesis, IL-17 signaling, inflammatory response, and interleukin signaling were potential pathways targeted by stevioside in T2D. Protein-protein interactions (PPI) analysis identified six common hub proteins (PPARG, PTGS2, CXCL8, CCL2, PTPRC, and EDN1). Molecular docking results showed best binding of stevioside to PPARG (-8 kcal/mol) and PTGS2 (-10.1 kcal/mol). Finally, 100 ns molecular dynamics demonstrated that the binding stability between stevioside and target protein (PPARG and PTGS2) falls within the acceptable range. CONCLUSIONS This study reveals that stevioside exhibits significant potential in controlling T2D by targeting key pathways and stably binding to PPARG and PTGS2. Further research is necessary to confirm and expand upon these significant computational results.
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Affiliation(s)
- Amit Dutta
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Md Arju Hossain
- Department of Microbiology, Primeasia University, Banani, Dhaka, 1213, Bangladesh
| | - Pratul Dipta Somadder
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Mahmuda Akter Moli
- Department of Pharmaceuticals and Industrial Biotechnology, Sylhet Agricultural University, Bangladesh
| | - Kawsar Ahmed
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University (MBSTU), Santosh, Tangail, 1902, Bangladesh; Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh.
| | - Md Masuder Rahman
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh.
| | - Francis M Bui
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
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Gomes FG, Boquett JA, Kowalski TW, Bremm JM, Michels MS, Pretto L, Rockenbach MK, Vianna FSL, Schuler-Faccini L, Sanseverino MTV, Fraga LR. From bench to in silico and backwards: What have we done on genetics of recurrent pregnancy loss and implantation failure and where should we go next? Genet Mol Biol 2024; 46:e20230127. [PMID: 39186710 PMCID: PMC11346592 DOI: 10.1590/1678-4685-gmb-2023-0127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 05/22/2024] [Indexed: 08/28/2024] Open
Abstract
Human reproduction goes through many challenges to its success and in many cases it fails. Cases of pregnancy loss are common outcomes for pregnancies, and implantation failures (IF) are common in assisted reproduction attempts. Although several risk factors have already been linked to adverse outcomes in reproduction, many cases remain without a definitive cause. Genetics of female reproduction is a field that may bring some pieces of this puzzle; however, there are no well-defined genes that might be related to the risk for recurrent pregnancy loss (RPL) and IF. Here, we present a literature review of the studies of genetic association in RPL and IF carried out in the Brazilian population and complemented with a database search to explore genes previously related to RPL and IF, where a search for genes previously involved in these conditions was performed in OMIM, HuGE, and CTD databases. Finally, we present the next steps for reproductive genetics investigation, through genomic sequencing analyses and discuss future plans in the study of RPL genetics. The combined strategy of looking for literature and databases is useful to raise hypotheses and to identify underexplored genes related to RPL and IF.
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Affiliation(s)
- Flavia Gobetti Gomes
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
| | - Juliano André Boquett
- Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Programa de Pós-Graduação em Saúde da Criança e do Adolescente, Porto Alegre, RS, Brazil
- University of California, Department of Neurology, San Francisco, CA, EUA
| | - Thayne Woycinck Kowalski
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Centro de Pesquisa Experimental, Laboratório de Medicina Genômica, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Núcleo de Bioinformática, Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Programa de Pós-Graduação em Medicina, Ciências Médicas, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Serviço de Genética Médica, Sistema Nacional de Informação sobre Agentes Teratogênicos (SIAT), Porto Alegre, RS, Brazil
| | - João Matheus Bremm
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
| | - Marcus Silva Michels
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
| | - Luiza Pretto
- Hospital de Clínicas de Porto Alegre (HCPA), Centro de Pesquisa Experimental, Laboratório de Medicina Genômica, Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Programa de Pós-Graduação em Medicina, Ciências Médicas, Porto Alegre, RS, Brazil
| | - Marília Körbes Rockenbach
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
| | - Fernanda Sales Luiz Vianna
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Centro de Pesquisa Experimental, Laboratório de Medicina Genômica, Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Programa de Pós-Graduação em Medicina, Ciências Médicas, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Serviço de Genética Médica, Sistema Nacional de Informação sobre Agentes Teratogênicos (SIAT), Porto Alegre, RS, Brazil
| | - Lavinia Schuler-Faccini
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Programa de Pós-Graduação em Saúde da Criança e do Adolescente, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Serviço de Genética Médica, Sistema Nacional de Informação sobre Agentes Teratogênicos (SIAT), Porto Alegre, RS, Brazil
| | - Maria Teresa Vieira Sanseverino
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Serviço de Genética Médica, Sistema Nacional de Informação sobre Agentes Teratogênicos (SIAT), Porto Alegre, RS, Brazil
- Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Escola de Medicina, Porto Alegre, Brazil
| | - Lucas Rosa Fraga
- Hospital de Clínicas de Porto Alegre (HCPA), Centro de Pesquisa Experimental, Laboratório de Medicina Genômica, Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Programa de Pós-Graduação em Medicina, Ciências Médicas, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre (HCPA), Serviço de Genética Médica, Sistema Nacional de Informação sobre Agentes Teratogênicos (SIAT), Porto Alegre, RS, Brazil
- Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Ciências Básicas da Saúde, Departamento de Ciências Morfológicas, Porto Alegre, RS, Brazil
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Musa I, Wang ZZ, Yang N, Li XM. Formononetin inhibits IgE by huPlasma/PBMCs and mast cells/basophil activation via JAK/STAT/PI3-Akt pathways. Front Immunol 2024; 15:1427563. [PMID: 39221239 PMCID: PMC11363073 DOI: 10.3389/fimmu.2024.1427563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Accepted: 07/15/2024] [Indexed: 09/04/2024] Open
Abstract
Rationale Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation. Methods In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin's effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines. Results Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. Conclusion These findings comprehensively present formononetin's mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.
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Affiliation(s)
- Ibrahim Musa
- Department of Pathology Microbiology & Immunology, New York Medical College, New York, NY, United States
| | - Zhen-Zhen Wang
- Department of Pathology Microbiology & Immunology, New York Medical College, New York, NY, United States
- Academy of Chinese Medical Science, Henan University of Chinese Medicine, Zhengzhou, China
| | - Nan Yang
- R&D Division, General Nutraceutical Technology LLC, Elmsford, NY, United States
| | - Xiu-Min Li
- Department of Pathology Microbiology & Immunology, New York Medical College, New York, NY, United States
- Department of Otolaryngology, School of Medicine, New York Medical College, New York, NY, United States
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9
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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues. Front Immunol 2024; 15:1428773. [PMID: 39161769 PMCID: PMC11330812 DOI: 10.3389/fimmu.2024.1428773] [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: 05/07/2024] [Accepted: 06/24/2024] [Indexed: 08/21/2024] Open
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- Institute of Computational Life Sciences, Zürich University of Applied Sciences (ZHAW), Wädenswil, Switzerland
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - María Rodríguez Martínez
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, United States
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10
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Arowolo O, Suvorov A. Underexplored Molecular Mechanisms of Toxicity. J Xenobiot 2024; 14:939-949. [PMID: 39051348 PMCID: PMC11270369 DOI: 10.3390/jox14030052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/01/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024] Open
Abstract
Social biases may concentrate the attention of researchers on a small number of well-known molecules/mechanisms leaving others underexplored. In accordance with this view, central to mechanistic toxicology is a narrow range of molecular pathways that are assumed to be involved in a significant part of the responses to toxicity. It is unclear, however, if there are other molecular mechanisms which play an important role in toxicity events but are overlooked by toxicology. To identify overlooked genes sensitive to chemical exposures, we used publicly available databases. First, we used data on the published chemical-gene interactions for 17,338 genes to estimate their sensitivity to chemical exposures. Next, we extracted data on publication numbers per gene for 19,243 human genes from the Find My Understudied Genes database. Thresholds were applied to both datasets using our algorithm to identify chemically sensitive and chemically insensitive genes and well-studied and underexplored genes. A total of 1110 underexplored genes highly sensitive to chemical exposures were used in GSEA and Shiny GO analyses to identify enriched biological categories. The metabolism of fatty acids, amino acids, and glucose were identified as underexplored molecular mechanisms sensitive to chemical exposures. These findings suggest that future effort is needed to uncover the role of xenobiotics in the current epidemics of metabolic diseases.
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Affiliation(s)
| | - Alexander Suvorov
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 686 North Pleasant Street, Amherst, MA 01003, USA;
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11
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Xin J, Wang T, Hou B, Lu X, Han N, He Y, Zhang D, Wang X, Wei C, Jia Z. Tongxinluo capsule as a multi-functional traditional Chinese medicine in treating cardiovascular disease: A review of components, pharmacological mechanisms, and clinical applications. Heliyon 2024; 10:e33309. [PMID: 39040283 PMCID: PMC11261786 DOI: 10.1016/j.heliyon.2024.e33309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/24/2024] Open
Abstract
Cardiovascular diseases (CVDs) are one of the most significant diseases that pose a threat to human health. The innovative traditional Chinese medicine Tongxinluo Capsule, developed under the guidance of the theory of traditional Chinese medicine, has good clinical efficacy in various cardiovascular diseases, this medicine has effects such as blood protection, vascular protection, myocardial protection, stabilizing vulnerable plaques, and vasodilation. However, CVDs are a multifactorial disease, and their underlying mechanisms are not fully understood. Therefore, exploring the mechanism of action and clinical application of Tongxinluo Capsule in the treatment of various cardiovascular diseases is beneficial for exerting its therapeutic effect from multiple components, targets, and pathways. At the same time, it provides broader treatment ideas for other difficult to treat diseases in the cardiovascular event chain, and has significant theoretical and clinical significance for improving the treatment of cardiovascular diseases with traditional Chinese medicine.
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Affiliation(s)
- Jingjing Xin
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Tongxing Wang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Bin Hou
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Xuan Lu
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Ningxin Han
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Yanling He
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050090, Hebei, China
| | - Dan Zhang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050090, Hebei, China
| | - Xiaoqi Wang
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Cong Wei
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Zhenhua Jia
- Graduate School, Hebei Medical University, Shijiazhuang, 050017, China
- State Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
- Affiliated Yiling Hospital of Hebei Medical University, High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Shijiazhuang, 050091, Hebei, China
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12
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Li Z, Zhang Q, Gao Y, Wan F, Wang Y, Hou B, Cui W, Wang Y, Feng W, Hou Y. Luobitong Potentiates MTX's Anti-Rheumatoid Arthritis Activity via Targeting Multiple Inflammatory Pathways. J Inflamm Res 2024; 17:4389-4403. [PMID: 38994468 PMCID: PMC11236762 DOI: 10.2147/jir.s461093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/18/2024] [Indexed: 07/13/2024] Open
Abstract
Background The LuoBiTong (LBT) capsule, a novel traditional Chinese medicine formulation, is currently in Phase III clinical trials. Preliminary preclinical and Phase II clinical studies suggest its efficacy and safety in treating rheumatoid arthritis (RA). However, the underlying mechanisms of its action remain to be elucidated.This research aims to explore the effects and mechanisms of LBT in conjunction with a maintenance dose of methotrexate (M-MTX) on RA. Methods A Collagen-Induced Arthritis (CIA) mouse model was used to evaluate the anti-RA effects of LBT combined with M-MTX. Assessments included foot swelling, arthritis scoring, serum inflammatory factor analysis, and histopathological examination of the foot. These effects were compared with those of high-dose MTX (H-MTX). Network pharmacology was employed to construct a compound-target network for RA, based on drug composition, to predict its potential mechanism of action. Flow cytometry, Western Blot, and immunohistochemical analyses in animal models identified multiple inflammatory pathways targeted by LBT to augment the anti-RA effects of MTX. Results The study revealed that LBT combined with M-MTX significantly alleviated CIA-induced arthritis without adverse effects. The combination of LBT and M-MTX showed similar or superior efficacy in regulating macrophage polarization, NF-κB, MAPK signaling pathways, and in the suppression of TH-17 expression in proinflammatory cells. These findings suggest that LBT may exert a multi-pathway therapeutic effect in RA treatment. The predicted pharmacological targets and mechanisms align well with this hypothesis. Conclusion LBT, when combined with MTX, enhances the anti-RA effect by targeting multiple inflammatory pathways, demonstrating significant therapeutic potential.
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Affiliation(s)
- Ziyu Li
- Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, People’s Republic of China
| | - Qiuyan Zhang
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Yuhe Gao
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, People’s Republic of China
| | - Fang Wan
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, People’s Republic of China
| | - Yincang Wang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, People’s Republic of China
| | - Bin Hou
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Wenwen Cui
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral VesselCollateral Disease), Shijiazhuang, People’s Republic of China
| | - Yanan Wang
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Wei Feng
- New Drug Evaluation Center, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
| | - Yunlong Hou
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang Yiling Pharmaceutical Co., Ltd, Shijiazhuang, People’s Republic of China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral VesselCollateral Disease), Shijiazhuang, People’s Republic of China
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13
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Jahan E, Mazumder T, Hasan T, Ahmed KS, Amanat M, Hossain H, Supty SJ, Liya IJ, Shuvo MSR, Daula AFMSU. Metabolomic Approach to Identify the Potential Metabolites from Alpinia malaccensis for Treating SARS-CoV-2 Infection. Biochem Genet 2024:10.1007/s10528-024-10869-4. [PMID: 38955878 DOI: 10.1007/s10528-024-10869-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 06/10/2024] [Indexed: 07/04/2024]
Abstract
The advent of the new coronavirus, leading to the SARS-CoV-2 pandemic, has presented a substantial worldwide health hazard since its inception in the latter part of 2019. The severity of the current pandemic is exacerbated by the occurrence of re-infection or co-infection with SARS-CoV-2. Hence, comprehending the molecular process underlying the pathophysiology of sepsis and discerning possible molecular targets for therapeutic intervention holds significant importance. For the first time, 31 metabolites were tentatively identified by GC-MS analysis from Alpinia malaccensis. On the other hand, five phenolic compounds were identified and quantified from the plant in HPLC-DAD analysis, including (-) epicatechin, rutin hydrate, rosmarinic acid, quercetin, and kaempferol. Nine GC-MS and five HPLC-identified metabolites had shown interactions with 45 and 30 COVID-19-associated human proteins, respectively. Among the proteins, PARP1, FN1, PRKCA, EGFR, ALDH2, AKR1C3, AHR, and IKBKB have been found as potential therapeutic targets to mitigate SARS-CoV-2 infection. KEGG pathway analysis also showed a strong association of FN1, EGFR, and IKBKB genes with SARS-CoV-2 viral replication and cytokine overexpression due to viral infection. Protein-protein interaction (PPI) analysis also showed that TP53, MMP9, FN1, EGFR, and NOS2 proteins are highly related to the genes involved in COVID-19 comorbidity. These proteins showed interaction with the plant phytoconstituents as well. As the study offers a robust network-based procedure for identifying biomolecules relevant to COVID-19 disease, A. malaccensis could be a good source of effective therapeutic agents against COVID-19 and related viral diseases.
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Affiliation(s)
- Esrat Jahan
- Department of Pharmacy, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh
| | - Tanoy Mazumder
- Department of Pharmacy, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh
| | - Tarek Hasan
- Department of Pharmacy, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh
| | - Khondoker Shahin Ahmed
- Chemical Research Division, Bangladesh Council of Scientific and Industrial Research, Dhaka, Bangladesh
| | - Muhammed Amanat
- Department of Pharmacy, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh
| | - Hemayet Hossain
- Chemical Research Division, Bangladesh Council of Scientific and Industrial Research, Dhaka, Bangladesh
| | - Sumaiya Jannat Supty
- Department of Soil, Water and Environment, University of Dhaka, Dhaka, Bangladesh
| | - Israt Jahan Liya
- Department of Pharmacy, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh
| | - Md Sadikur Rahman Shuvo
- Department of Microbiology, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh.
| | - A F M Shahid Ud Daula
- Department of Pharmacy, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh.
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14
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Kim YW, Bak SB, Song YR, Kim CE, Lee WY. Systematic exploration of therapeutic effects and key mechanisms of Panax ginseng using network-based approaches. J Ginseng Res 2024; 48:373-383. [PMID: 39036729 PMCID: PMC11258513 DOI: 10.1016/j.jgr.2024.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/30/2023] [Accepted: 01/19/2024] [Indexed: 07/23/2024] Open
Abstract
Background Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.
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Affiliation(s)
- Young Woo Kim
- School of Korean Medicine, Dongguk University, Gyeongju, Republic of Korea
- Department of Computer Science, Kyungpook National University, Daegu, Republic of Korea
| | - Seon Been Bak
- School of Korean Medicine, Dongguk University, Gyeongju, Republic of Korea
| | - Yu Rim Song
- School of Korean Medicine, Dongguk University, Gyeongju, Republic of Korea
| | - Chang-Eop Kim
- School of Korean Medicine, Gachon University, Seongnam, Republic of Korea
| | - Won-Yung Lee
- School of Korean Medicine, Dongguk University, Gyeongju, Republic of Korea
- School of Korean Medicine, Wonkwang University, Iksan, Republic of Korea
- Research Center of Traditional Korean Medicine, Wonkwang University, Republic of Korea
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15
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Li N, Yang Z, Wang J, Lin H. Drug-target interaction prediction using knowledge graph embedding. iScience 2024; 27:109393. [PMID: 38952679 PMCID: PMC11215290 DOI: 10.1016/j.isci.2024.109393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/16/2024] [Accepted: 02/28/2024] [Indexed: 07/03/2024] Open
Abstract
The prediction of drug-target interactions (DTIs) is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. Computational approaches to predicting DTIs can provide important insights into drug mechanisms of action. However, current methods for predicting DTIs based on the structural information of the knowledge graph may suffer from the sparseness and incompleteness of the knowledge graph and neglect the latent type information of the knowledge graph. In this paper, we propose TTModel, a knowledge graph embedding model for DTI prediction. By exploiting biomedical text and type information, TTModel can learn latent text semantics and type information to improve the performance of representation learning. Comprehensive experiments on two public datasets demonstrate that our model outperforms the state-of-the-art methods significantly on the task of DTI prediction.
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Affiliation(s)
- Nan Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Jian Wang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
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16
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Zafeiropoulos S, Ahmed U, Bekiaridou A, Jayaprakash N, Mughrabi IT, Saleknezhad N, Chadwick C, Daytz A, Kurata-Sato I, Atish-Fregoso Y, Carroll K, Al-Abed Y, Fudim M, Puleo C, Giannakoulas G, Nicolls MR, Diamond B, Zanos S. Ultrasound Neuromodulation of an Anti-Inflammatory Pathway at the Spleen Improves Experimental Pulmonary Hypertension. Circ Res 2024; 135:41-56. [PMID: 38712557 DOI: 10.1161/circresaha.123.323679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Inflammation is pathogenically implicated in pulmonary arterial hypertension; however, it has not been adequately targeted therapeutically. We investigated whether neuromodulation of an anti-inflammatory neuroimmune pathway involving the splenic nerve using noninvasive, focused ultrasound stimulation of the spleen (sFUS) can improve experimental pulmonary hypertension. METHODS Pulmonary hypertension was induced in rats either by Sugen 5416 (20 mg/kg SQ) injection, followed by 21 (or 35) days of hypoxia (sugen/hypoxia model), or by monocrotaline (60 mg/kg IP) injection (monocrotaline model). Animals were randomized to receive either 12-minute-long sessions of sFUS daily or sham stimulation for 14 days. Catheterizations, echocardiography, indices of autonomic function, lung and heart histology and immunohistochemistry, spleen flow cytometry, and lung single-cell RNA sequencing were performed after treatment to assess the effects of sFUS. RESULTS Splenic denervation right before induction of pulmonary hypertension results in a more severe disease phenotype. In both sugen/hypoxia and monocrotaline models, sFUS treatment reduces right ventricular systolic pressure by 25% to 30% compared with sham treatment, without affecting systemic pressure, and improves right ventricular function and autonomic indices. sFUS reduces wall thickness, apoptosis, and proliferation in small pulmonary arterioles, suppresses CD3+ and CD68+ cell infiltration in lungs and right ventricular fibrosis and hypertrophy and lowers BNP (brain natriuretic peptide). Beneficial effects persist for weeks after sFUS discontinuation and are more robust with early and longer treatment. Splenic denervation abolishes sFUS therapeutic benefits. sFUS partially normalizes CD68+ and CD8+ T-cell counts in the spleen and downregulates several inflammatory genes and pathways in nonclassical and classical monocytes and macrophages in the lung. Differentially expressed genes in those cell types are significantly enriched for human pulmonary arterial hypertension-associated genes. CONCLUSIONS sFUS causes dose-dependent, sustained improvement of hemodynamic, autonomic, laboratory, and pathological manifestations in 2 models of experimental pulmonary hypertension. Mechanistically, sFUS normalizes immune cell populations in the spleen and downregulates inflammatory genes and pathways in the lung, many of which are relevant in human disease.
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Affiliation(s)
- Stefanos Zafeiropoulos
- Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY (S. Zafeiropoulos, A.B., Y.A.-A., G.G., S. Zanos)
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Umair Ahmed
- Department of Neurology, Staten Island University Hospital, Staten Island, NY (U.A.)
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Alexandra Bekiaridou
- Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY (S. Zafeiropoulos, A.B., Y.A.-A., G.G., S. Zanos)
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Naveen Jayaprakash
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Ibrahim T Mughrabi
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Nafiseh Saleknezhad
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | | | - Anna Daytz
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Izumi Kurata-Sato
- Institute of Molecular Medicine (I.K.-S., Y.A.-F., K.C., B.D.), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Yemil Atish-Fregoso
- Institute of Molecular Medicine (I.K.-S., Y.A.-F., K.C., B.D.), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Kaitlin Carroll
- Institute of Molecular Medicine (I.K.-S., Y.A.-F., K.C., B.D.), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Yousef Al-Abed
- Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY (S. Zafeiropoulos, A.B., Y.A.-A., G.G., S. Zanos)
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
| | - Marat Fudim
- Division of Cardiology, Duke University Medical Center, Durham, NC (M.F.)
- Duke Clinical Research Institute, Durham, NC (M.F.)
| | | | - George Giannakoulas
- Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY (S. Zafeiropoulos, A.B., Y.A.-A., G.G., S. Zanos)
- Department of Cardiology, AHEPA University Hospital, Aristotle University School of Medicine, Thessaloniki, Greece (G.G.)
| | - Mark R Nicolls
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Stanford University, CA (M.R.N.)
| | - Betty Diamond
- Institute of Molecular Medicine (I.K.-S., Y.A.-F., K.C., B.D.), Feinstein Institutes for Medical Research, Manhasset, NY
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY (B.D., S. Zanos)
| | - Stavros Zanos
- Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY (S. Zafeiropoulos, A.B., Y.A.-A., G.G., S. Zanos)
- Institute of Bioelectronic Medicine (S. Zafeiropoulos, U.A., A.B., N.J., I.T.M., N.S., A.D., Y.A.-A., S. Zanos), Feinstein Institutes for Medical Research, Manhasset, NY
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY (B.D., S. Zanos)
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Baralić K, Božović P, Đukić-Ćosić D. Deciphering the molecular landscape of ionising radiation-induced eye damage with the help of genomic data mining. Arh Hig Rada Toksikol 2024; 75:91-101. [PMID: 38963141 PMCID: PMC11223508 DOI: 10.2478/aiht-2024-75-3817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 01/01/2024] [Accepted: 05/01/2024] [Indexed: 07/05/2024] Open
Abstract
Even at low levels, exposure to ionising radiation can lead to eye damage. However, the underlying molecular mechanisms are not yet fully understood. We aimed to address this gap with a comprehensive in silico approach to the issue. For this purpose we relied on the Comparative Toxicogenomics Database (CTD), ToppGene Suite, Cytoscape, GeneMANIA, and Metascape to identify six key regulator genes associated with radiation-induced eye damage (ATM, CRYAB, SIRT1, TGFB1, TREX1, and YAP1), all of which have physical interactions. Some of the identified molecular functions revolve around DNA repair mechanisms, while others are involved in protein binding, enzymatic activities, metabolic processes, and post-translational protein modifications. The biological processes are mostly centred on response to DNA damage, the p53 signalling pathway in particular. We identified a significant role of several miRNAs, such as hsa-miR-183 and hsamiR-589, in the mechanisms behind ionising radiation-induced eye injuries. Our study offers a valuable method for gaining deeper insights into the adverse effects of radiation exposure.
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Affiliation(s)
- Katarina Baralić
- University of Belgrade, Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović“, Belgrade, Serbia
| | - Predrag Božović
- University of Belgrade Vinča Institute of Nuclear Sciences, Department of Radiation and Environmental Protection, Belgrade, Serbia
| | - Danijela Đukić-Ćosić
- University of Belgrade, Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović“, Belgrade, Serbia
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Khan WH, Ahmad R, Alam R, Khan N, Rather IA, Wani MY, Singh RB, Ahmad A. Role of ribosomal pathways and comorbidity in COVID-19: Insight from SARS-CoV-2 proteins and host proteins interaction network analysis. Heliyon 2024; 10:e29967. [PMID: 38694063 PMCID: PMC11059120 DOI: 10.1016/j.heliyon.2024.e29967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
The COVID-19 pandemic has become a significant global issue in terms of public health. While it is largely associated with respiratory complications, recent reports indicate that patients also experience neurological symptoms and other health issues. The objective of this study is to examine the network of protein-protein interactions (PPI) between SARS-CoV-2 proteins and human host proteins, pinpoint the central genes within this network implicated in disease pathology, and assess their viability as targets for drug development. The study adopts a network-based approach to construct a network of 29 SARS-CoV-2 proteins interacting with 2896 host proteins, with 176 host genes being identified as interacting genes with all the viral proteins. Gene ontology and pathway analysis of these host proteins revealed their role in biological processes such as translation, mRNA splicing, and ribosomal pathways. We further identified EEF2, RPS3, RPL9, RPS16, and RPL11 as the top 5 most connected hub genes in the disease-causing network, with significant interactions among each other. These hub genes were found to be involved in ribosomal pathways and cytoplasmic translation. Further a disease-gene interaction was also prepared to investigate the role of hub genes in other disorders and to understand the condition of comorbidity in COVID-19 patients. We also identified 13 drug molecules having interactions with all the hub genes, and estradiol emerged as the top potential drug target for the COVID-19 patients. Our study provides valuable insights using the protein-protein interaction network of SARS-CoV-2 proteins with host proteins and highlights the molecular basis of manifestation of COVID-19 and proposes drug for repurposing. As the pandemic continues to evolve, it is anticipated that investigating SARS-CoV-2 proteins will remain a critical area of focus for researchers globally, particularly in addressing potential challenges posed by specific SARS-CoV-2 variants in the future.
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Affiliation(s)
- Wajihul Hasan Khan
- Department of Microbiology, All India Institute of Medical Sciences, Delhi, 110029, India
| | - Razi Ahmad
- Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Ragib Alam
- Department of Microbiology, All India Institute of Medical Sciences, Delhi, 110029, India
| | - Nida Khan
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Irfan A. Rather
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Mohmmad Younus Wani
- Department of Chemistry, College of Science, University of Jeddah, Jeddah, 21589, Saudi Arabia
| | - R.K. Brojen Singh
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Aijaz Ahmad
- Clinical Microbiology and Infectious Diseases, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 2193, South Africa
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, 15213, USA
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Wang J, Sun H, Mou L, Lu Y, Wu Z, Pu Z, Yang MM. Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning. Front Endocrinol (Lausanne) 2024; 15:1382896. [PMID: 38800474 PMCID: PMC11116564 DOI: 10.3389/fendo.2024.1382896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Background Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR. Methods We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs. Results Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis. Conclusion This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.
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Affiliation(s)
- Jun Wang
- Department of Endocrinology, Shenzhen People’s Hospital (The Second Clinical Medical College of Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Hongyan Sun
- Department of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Lisha Mou
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Ying Lu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Zijing Wu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Zuhui Pu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, Guangdong, Shenzhen, China
| | - Ming-ming Yang
- Department of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
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Wang Y, Song J, Dai Q, Duan X. Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction. IEEE J Biomed Health Inform 2024; 28:3146-3157. [PMID: 38294927 DOI: 10.1109/jbhi.2024.3360437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.
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Ren H, Wu W, Chen J, Li Q, Wang H, Qian D, Guo S, Duan JA. Integrated serum metabolomics and network pharmacology analysis on the bioactive metabolites and mechanism exploration of Bufei huoxue capsule on chronic obstructive pulmonary disease rats. JOURNAL OF ETHNOPHARMACOLOGY 2024; 324:117816. [PMID: 38286154 DOI: 10.1016/j.jep.2024.117816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/09/2024] [Accepted: 01/21/2024] [Indexed: 01/31/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Bufei Huoxue capsule (BHC) as a classic Chinese patent medicine formula, has the efficacy of tonifying the lungs and activating the blood. It has been extensively used in China for the treatment of chronic obstructive pulmonary disease (COPD) clinically. However, its mechanism is still unclear, which hampers the applications of BHC in treating COPD. AIM OF THE STUDY The purpose of the present study was to demonstrate the protective efficacy and mechanism of BHC on COPD model rats by integrating serum metabolomics analysis and network pharmacology study. MATERIALS AND METHODS A COPD rat model was established by cigarette fumigation combined with lipopolysaccharide (LPS) airway drip for 90 consecutive days. After oral administration for 30 days, the rats were placed in the body tracing box of the EMKA Small Animal Noninvasive Lung Function Test System to determine lung function related indexes. Histopathological alteration was observed by H&E staining and Masson staining. The serum levels of inflammatory cytokine, matrix metalloprotein 9, and laminin were determined by ELISA kits. Oxidative stress levels were tested by biochemical methods. UHPLC-Q-TOF/MS analysis of serum metabolomics and network pharmacology were performed to reveal the bioactive metabolites, key components and pathways for BHC treating COPD. WB and ELISA kits were used to verify the effects of BHC on key pathway. RESULTS BHC could improve lung function, immunity, lung histopathological changes and collagen deposition in COPD model rats. It also could significantly reduce inflammatory response in vivo, regulate oxidative stress level, reduce laminin content, and regulate protease-antiprotease balance. Metabolomics analysis found 46 biomarkers of COPD, of which BHC significantly improved the levels of 23 differential metabolites including arachidonic acid, leukotriene B4 and prostaglandin E2. Combined with the results of network pharmacology, the components of BHC, such as calycosin, oxypaeoniflora, (S)-bavachin and neobavaisoflavone could play therapeutic roles through the arachidonic acid pathway. In addition, the results of WB and ELISA indicated that BHC could suppress the expressions of COX2 and 5-LOX in lung tissues and inhibit the generation of AA and its metabolites in serum samples. Regulation of arachidonic acid metabolic pathway may be the crucial mechanism for BHC treating COPD. CONCLUSIONS In summary, the studies indicated that BHC exhibited the protective effect on COPD model rats by anti-inflammatory and anti-oxidative properties through arachidonic acid metabolism pathway. This study provided beneficial support for the applications of BHC in treating COPD.
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Affiliation(s)
- Hui Ren
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization and Jiangsu Key Laboratory for High Technology Research of Traditional Chinese Medicine Formulae, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Wenxing Wu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization and Jiangsu Key Laboratory for High Technology Research of Traditional Chinese Medicine Formulae, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Jiangyan Chen
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization and Jiangsu Key Laboratory for High Technology Research of Traditional Chinese Medicine Formulae, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Quan Li
- Leiyunshang Pharmaceutical Co. Limited, Suzhou, 215003, China
| | - Hengbin Wang
- Leiyunshang Pharmaceutical Co. Limited, Suzhou, 215003, China
| | - Dawei Qian
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization and Jiangsu Key Laboratory for High Technology Research of Traditional Chinese Medicine Formulae, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Sheng Guo
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization and Jiangsu Key Laboratory for High Technology Research of Traditional Chinese Medicine Formulae, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| | - Jin-Ao Duan
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization and Jiangsu Key Laboratory for High Technology Research of Traditional Chinese Medicine Formulae, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
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Alam MJ, Rahman MH, Hossain MA, Hoque MR, Aktaruzzaman M. Bioinformatics and Systems Biology Approaches to Identify the Synergistic Effects of Alcohol Use Disorder on the Progression of Neurological Diseases. Neuroscience 2024; 543:65-82. [PMID: 38401711 DOI: 10.1016/j.neuroscience.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 02/26/2024]
Abstract
Clinical investigations showed that individuals with Alcohol Use Disorder (AUD) have worse Neurological Disease (ND) development, pointing to possible pathogenic relationships between AUD and NDs. It remains difficult to identify risk factors that are predisposing between AUD and NDs. In order to fix these issues, we created the bioinformatics pipeline and network-based approaches for employing unbiased methods to discover genes abnormally stated in both AUD and NDs and to pinpoint some of the common molecular pathways that might underlie AUD and ND interaction. We found 100 differentially expressed genes (DEGs) in both the AUD and ND patient's tissue samples. The most important Gene Ontology (GO) terms and metabolic pathways, including positive control of cytotoxicity caused by T cells, proinflammatory responses, antigen processing and presentation, and platelet-triggered interactions with vascular and circulating cell pathways were then extracted using the overlapped DEGs. Protein-protein interaction analysis was used to identify hub proteins, including CCL2, IL1B, TH, MYCN, HLA-DRB1, SLC17A7, and HNF4A, in the pathways that have been reported as playing a function in these disorders. We determined several TFs (HNF4A, C4A, HLA-B, SNCA, HLA-DMB, SLC17A7, HLA-DRB1, HLA-C, HLA-A, and HLA-DPB1) and potential miRNAs (hsa-mir-34a-5p, hsa-mir-34c-5p, hsa-mir-449a, hsa-mir-155-5p, and hsa-mir-1-3p) were crucial for regulating the expression of AUD and ND which could serve as prospective targets for treatment. Our methodologies discovered unique putative biomarkers that point to the interaction between AUD and various neurological disorders, as well as pathways that could one day be the focus of therapeutic intervention.
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Affiliation(s)
- Md Jahangir Alam
- Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh; Center for Advanced Bioinformatics and Artificial Intelligence Research, Islamic University, Kushtia 7003, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh; Center for Advanced Bioinformatics and Artificial Intelligence Research, Islamic University, Kushtia 7003, Bangladesh.
| | - Md Arju Hossain
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh; Department of Microbiology, Primeasia University, Banani, Dhaka 1213, Bangladesh
| | - Md Robiul Hoque
- Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh
| | - Md Aktaruzzaman
- Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh
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Han SY, Kim JH, Bae GS, Lee WY. Identifying Candidate Polyphenols Beneficial for Oxidative Liver Injury through Multiscale Network Analysis. Curr Issues Mol Biol 2024; 46:3081-3091. [PMID: 38666923 PMCID: PMC11049334 DOI: 10.3390/cimb46040193] [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: 03/11/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024] Open
Abstract
Oxidative stress, a driver of liver pathology, remains a challenge in clinical management, necessitating innovative approaches. In this research, we delved into the therapeutic potential of polyphenols for oxidative liver injury using a multiscale network analysis framework. From the Phenol-Explorer database, we curated a list of polyphenols along with their corresponding PubChem IDs. Verified target information was then collated from multiple databases. We subsequently measured the propagative effects of these compounds and prioritized a ranking based on their correlation scores for oxidative liver injury. This result underwent evaluation to discern its effectiveness in differentiating between known and unknown polyphenols, demonstrating superior performance over chance level in distinguishing these compounds. We found that lariciresinol and isopimpinellin yielded high correlation scores in relation to oxidative liver injury without reported evidence. By analyzing the impact on a multiscale network, we found that lariciresinol and isopimpinellin were predicted to offer beneficial effects on the disease by directly acting on targets such as CASP3, NR1I2, and CYP3A4 or by modulating biological functions related to the apoptotic process and oxidative stress. This study not only corroborates the efficacy of identified polyphenols in liver health but also opens avenues for future investigations into their mechanistic actions.
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Affiliation(s)
- Sang Yun Han
- The Office of Korean Medicine Education, College of Korean Medicine, Daejeon University, Daejeon 34530, Republic of Korea
| | - Ji-Hwan Kim
- Department of Sasang Constitutional Medicine, Division of Clinical Medicine, School of Korean Medicine, Pusan National University, Busan 46241, Republic of Korea
| | - Gi-Sang Bae
- Department of Pharmacology, College of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea
- Research Center of Traditional Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea
| | - Won-Yung Lee
- Research Center of Traditional Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea
- Department of Pathology, College of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea
- Department of Pathology, College of Korean Medicine, Woosuk University, Jeon-Ju 54987, Republic of Korea
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Mogal MR, Jame JA, Sohel M, Mozibullah M, Mahmod MR, Junayed A, Kar N, Arbia L, Al Mamun A, Sikder MA. Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies. Cancer Rep (Hoboken) 2024; 7:e2059. [PMID: 38639039 PMCID: PMC11027013 DOI: 10.1002/cnr2.2059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/20/2024] [Accepted: 03/24/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications. METHODS Three microarray datasets were sourced from the NCBI database to acquire upregulated differentially expressed genes (DEGs), while MatrisomeDB was employed for filtering ECM-related genes. Subsequently, a protein-protein interaction (PPI) network was established using the STRING database. The created network was visually inspected through Cytoscape, and HGs were identified using the CytoHubba plugin tool. Furthermore, enrichment analysis, expression pattern analysis, clinicopathological correlation, survival analysis, immune cell infiltration analysis, and examination of chemical compounds were carried out using Enrichr, GEPIA2, ULCAN, Kaplan Meier plotter, TIMER2.0, and CTD web platforms, respectively. The diagnostic and prognostic significance of HGs was evaluated through the ROC curve analysis. RESULTS Ten genes associated with ECM functions were identified as HGs among 131 DEGs obtained from microarray datasets. Notably, the expression of these HGs exhibited significantly (p < 0.05) higher in PC, demonstrating a clear association with tumor advancement. Remarkably, higher expression levels of these HGs were inversely correlated with the likelihood of patient survival. Moreover, ROC curve analysis revealed that identified HGs are promising biomarkers for both diagnostic (AUC > 0.75) and prognostic (AUC > 0.64) purposes. Furthermore, we observed a positive correlation between immune cell infiltration and the expression of most HGs. Lastly, our study identified nine compounds with significant interaction profiles that could potentially act as effective chemical agents targeting the identified HGs. CONCLUSION Taken together, our findings suggest that COL1A1, KRT19, MMP1, COL11A1, SDC1, ITGA2, COL1A2, POSTN, FN1, and COL5A1 hold promise as innovative biomarkers for both the diagnosis and prognosis of PC, and they present as prospective targets for therapeutic interventions aimed at impeding the progression PC.
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Affiliation(s)
- Md Roman Mogal
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Jasmin Akter Jame
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Md Sohel
- Department of Biochemistry and Molecular BiologyPrimeasia UniversityDhakaBangladesh
| | - Md Mozibullah
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Md Rashel Mahmod
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Asadullah Junayed
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Newton Kar
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Lubatul Arbia
- Department of Biochemistry and Molecular BiologyPrimeasia UniversityDhakaBangladesh
| | - Abdullah Al Mamun
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Md Asaduzzaman Sikder
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
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Zhang Y, Wang T, Song Y, Chen M, Hou B, Yao B, Ma K, Song Y, Wang S, Zhang D, Liang J, Wei C. Mechanism of Bazi Bushen capsule in delaying the senescence of mesenchymal stem cells based on network pharmacology and experimental validation. Heliyon 2024; 10:e27646. [PMID: 38509951 PMCID: PMC10950659 DOI: 10.1016/j.heliyon.2024.e27646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/22/2024] Open
Abstract
Ageing is becoming an increasingly serious problem; therefore, there is an urgent need to find safe and effective anti-ageing drugs. Aims To investigate the effects of Bazi Bushen capsule (BZBS) on the senescence of mesenchymal stem cells (MSCs) and explore its mechanism of action. Methods Network pharmacology was used to predict the targets of BZBS in delaying senescence in MSCs. For in vitro studies, MSCs were treated with D-gal, BZBS, and NMN, and cell viability, cell senescence, stemness-related genes, and cell cycle were studied using cell counting kit-8 (CCK-8) assay, SA-β-galactosidase (SA-β-gal) staining, Quantitative Real-Time PCR (qPCR) and flow cytometry (FCM), respectively. Alkaline phosphatase (ALP), alizarin red, and oil red staining were used to determine the osteogenic and lipid differentiation abilities of MSCs. Finally, the expression of senescence-related genes and cyclin-related factors was detected by qPCR and western blotting. Results Network pharmacological analysis suggested that BZBS delayed cell senescence by interfering in the cell cycle. Our in vitro studies suggested that BZBS could significantly increase cell viability (P < 0.01), decrease the quantity of β-galactosidase+ cells (P < 0.01), downregulate p16 and p21 (P < 0.05, P < 0.01), improve adipogenic and osteogenic differentiation, and upregulate Nanog, OCT4 and SOX2 genes (P < 0.05, P < 0.01) in senescent MSCs. Moreover, BZBS significantly reduced the proportion of senescent MSCs in the G0/G1 phase (P < 0.01) and enhanced the expression of CDK4, Cyclin D1, and E2F1 (P < 0.05, P < 0.01, respectively). Upon treatment with HY-50767A, a CDK4 inhibitor, the upregulation of E2F1 was no longer observed in the BZBS group. Conclusions BZBS can protect MSCs against D-gal-induced senescence, which may be associated with cell cycle regulation via the Cyclin D1/CDK4/E2F1 signalling pathway.
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Affiliation(s)
- Yaping Zhang
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050091, China
| | - Tongxing Wang
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
- Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Disease), Shijiazhuang, 050035, China
| | - Yanfei Song
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
- Shijiazhuang Compound Traditional Chinese Medicine Technology Innovation Center, Shijiazhuang, 050035, China
| | - Meng Chen
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
- Shijiazhuang Compound Traditional Chinese Medicine Technology Innovation Center, Shijiazhuang, 050035, China
| | - Bin Hou
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
| | - Bing Yao
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
- Shijiazhuang Compound Traditional Chinese Medicine Technology Innovation Center, Shijiazhuang, 050035, China
| | - Kun Ma
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
- Hebei Clinical Research Center of Cardiovascular Disease of Traditional Chinese Medicine, Shijiazhuang, 050035, China
| | - Yahui Song
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
| | - Siwei Wang
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050091, China
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
| | - Dan Zhang
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050091, China
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
| | - Junqing Liang
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
| | - Cong Wei
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050091, China
- National Key Laboratory for Innovation and Transformation of Luobing Theory, Shijiazhuang, 050035, China
- High-level TCM Key Disciplines of National Administration of Traditional Chinese Medicine—Luobing Theory, Hebei Province, Shijiazhuang, 050035, China
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Xu J, Zhu L, Xu J, Lin K, Wang J, Bi YL, Xu GT, Tian H, Gao F, Jin C, Lu L. The identification of a novel shared therapeutic target and drug across all insulin-sensitive tissues under insulin resistance. Front Nutr 2024; 11:1381779. [PMID: 38595789 PMCID: PMC11002099 DOI: 10.3389/fnut.2024.1381779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/15/2024] [Indexed: 04/11/2024] Open
Abstract
Background To identify key and shared insulin resistance (IR) molecular signatures across all insulin-sensitive tissues (ISTs), and their potential targeted drugs. Methods Three datasets from Gene Expression Omnibus (GEO) were acquired, in which the ISTs (fat, muscle, and liver) were from the same individual with obese mice. Integrated bioinformatics analysis was performed to obtain the differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was carried out to determine the "most significant trait-related genes" (MSTRGs). Enrichment analysis and PPI network were performed to find common features and novel hub genes in ISTs. The shared genes of DEGs and genes between DEGs and MSTRGs across four ISTs were identified as key IR therapeutic target. The Attie Lab diabetes database and obese rats were used to verify candidate genes. A medical drug-gene interaction network was conducted by using the Comparative Toxicogenomics Database (CTD) to find potential targeted drugs. The candidate drug was validated in Hepa1-6 cells. Results Lipid metabolic process, mitochondrion, and oxidoreductase activity as common features were enriched from ISTs under an obese context. Thirteen shared genes (Ubd, Lbp, Hp, Arntl, Cfd, Npas2, Thrsp., Tpx2, Pkp1, Sftpd, Mthfd2, Tnfaip2, and Vnn3) of DEGs across ISTs were obtained and confirmed. Among them, Ubd was the only shared gene between DEGs and MSTRGs across four ISTs. The expression of Ubd was significantly upregulated across four ISTs in obese rats, especially in the liver. The IR Hepa1-6 cell models treated with dexamethasone (Dex), palmitic acid (PA), and 2-deoxy-D-ribose (dRib) had elevated expression of Ubd. Knockdown of Ubd increased the level of p-Akt. A lowing Ubd expression drug, promethazine (PMZ) from CTD analysis rescued the decreased p-Akt level in IR Hepa1-6 cells. Conclusion This study revealed Ubd, a novel and shared IR molecular signature across four ISTs, as an effective biomarker and provided new insight into the mechanisms of IR. PMZ was a candidate drug for IR which increased p-Akt level and thus improved IR by targeting Ubd and downregulation of Ubd expression. Both Ubd and PMZ merit further clinical translational investigation to improve IR.
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Affiliation(s)
- Jinyuan Xu
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Biochemistry and Molecular Biology, School of Medicine, Tongji University, Shanghai, China
| | - Lilin Zhu
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Biochemistry and Molecular Biology, School of Medicine, Tongji University, Shanghai, China
| | - Jie Xu
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Biochemistry and Molecular Biology, School of Medicine, Tongji University, Shanghai, China
| | - Kailong Lin
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Biochemistry and Molecular Biology, School of Medicine, Tongji University, Shanghai, China
| | - Juan Wang
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Genetics, Tongji University School of Medicine, Shanghai, China
| | - Yan-long Bi
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
| | - Guo-Tong Xu
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
| | - Haibin Tian
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Ophthalmology of Ten People Hospital Affiliated to Tongji University, School of Medicine, Shanghai, China
| | - Furong Gao
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Biochemistry and Molecular Biology, School of Medicine, Tongji University, Shanghai, China
| | - Caixia Jin
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Biochemistry and Molecular Biology, School of Medicine, Tongji University, Shanghai, China
| | - Lixia Lu
- Department of Ophthalmology, Shanghai Tongji Hospital Affiliated to Tongji University, School of Medicine, Tongji Eye Institute, Shanghai, China
- Department of Biochemistry and Molecular Biology, School of Medicine, Tongji University, Shanghai, China
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Zou HT, Ji BY, Xie XL. A multi-source molecular network representation model for protein-protein interactions prediction. Sci Rep 2024; 14:6184. [PMID: 38485942 PMCID: PMC10940665 DOI: 10.1038/s41598-024-56286-w] [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: 11/07/2023] [Accepted: 03/05/2024] [Indexed: 03/18/2024] Open
Abstract
The prediction of potential protein-protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein-protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein-protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein-protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs .
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Affiliation(s)
- Hai-Tao Zou
- College of Information Science and Engineering, Guilin University of Technology, Guilin, 541000, China
| | - Bo-Ya Ji
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China.
| | - Xiao-Lan Xie
- College of Information Science and Engineering, Guilin University of Technology, Guilin, 541000, China.
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Bai X, Zheng E, Tong L, Liu Y, Li X, Yang H, Jiang J, Chang Z, Yang H. Angong Niuhuang Wan inhibit ferroptosis on ischemic and hemorrhagic stroke by activating PPARγ/AKT/GPX4 pathway. JOURNAL OF ETHNOPHARMACOLOGY 2024; 321:117438. [PMID: 37984544 DOI: 10.1016/j.jep.2023.117438] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Angong Niuhuang Wan (AGNHW) is a prescription from traditional Chinese medicine (TCM) that has been used for centuries to treat ischemic stroke (IS) and hemorrhagic stroke (HS). According to a recent study, targeting ferroptosis might be effective in the management of IS and HS. However, the ferroptosis-related effects and mechanisms of AGNHW have not yet been reported. AIM OF THE STUDY This research examines the anti-ferroptosis mechanisms of AGNHW in the treatment of IS and HS. MATERIALS AND METHODS A system pharmacological approach including in vivo experiment, UHPLC-Q-Orbitrap HRMS, network pharmacology, molecular docking, microscale thermophoresis, and in vitro experiment was utilized to study the anti-ferroptosis mechanisms of AGNHW against IS and HS. RESULTS In vivo experiments indicated that AGNHW enhanced nerve function, decreased cerebral infarct volume, ameliorated histological brain injuries, improved the structural integrity of the blood-brain barrier, ameliorated the mitochondrial dysfunction and morphology disruption, and inhibits ROS, LPO and Fe2+ accumulations in IS and HS rats. Using UHPLC-Q-Orbitrap HRMS, the key ingredients of AGNHW-containing serum were identified as bilirubin, berberine, baicalin, and wogonoside. According to the network pharmacology analyses, AGNHW could inhibit ferroptosis by modulating the PPAR and PI3K/AKT signaling pathways. The core targets are PPARγ, AKT, and GPX4. Molecular docking and microscale thermophoresis experiments further revealed that the key ingredients have strong interactions with ferroptosis-regulating core proteins. Moreover, in vitro experiment results showed that AGNHW alleviated ferroptosis injury induced by erastin in PC12 cells, increased cell viability, reduced the LPO and Fe2+ levels, and up-regulated mRNA expressions of PPARγ, AKT, and GPX4. AGNHW also up-regulated protein expressions of PPARγ, p-AKT/AKT, and GPX4 in IS and HS rats. CONCLUSIONS AGNHW attenuated ferroptosis in treating IS and HS by targeting the PPARγ/AKT/GPX4 pathway. This work reveals AGNHW's anti-ferroptosis mechanism against IS and HS, but it also develops an integrated approach to demonstrate the common characteristics of drugs in treating different diseases.
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Affiliation(s)
- Xue Bai
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment of Major Disease, Beijing, 100700, China.
| | - Enqi Zheng
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment of Major Disease, Beijing, 100700, China; Henan University of Chinese Medicine, Henan, 450046, China
| | - Lin Tong
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Yang Liu
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment of Major Disease, Beijing, 100700, China
| | - Xianyu Li
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment of Major Disease, Beijing, 100700, China
| | - Hong Yang
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment of Major Disease, Beijing, 100700, China
| | - Jie Jiang
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment of Major Disease, Beijing, 100700, China
| | - Zhenghui Chang
- Henan University of Chinese Medicine, Henan, 450046, China
| | - Hongjun Yang
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment of Major Disease, Beijing, 100700, China.
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Niu X, Xu C, Cheuk YC, Xu X, Liang L, Zhang P, Rong R. Characterizing hub biomarkers for post-transplant renal fibrosis and unveiling their immunological functions through RNA sequencing and advanced machine learning techniques. J Transl Med 2024; 22:186. [PMID: 38378674 PMCID: PMC10880303 DOI: 10.1186/s12967-024-04971-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/09/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Kidney transplantation stands out as the most effective renal replacement therapy for patients grappling with end-stage renal disease. However, post-transplant renal fibrosis is a prevalent and irreversible consequence, imposing a substantial clinical burden. Unfortunately, the clinical landscape remains devoid of reliable biological markers for diagnosing post-transplant renal interstitial fibrosis. METHODS We obtained transcriptome and single-cell sequencing datasets of patients with renal fibrosis from NCBI Gene Expression Omnibus (GEO). Subsequently, we employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify potential genes by integrating core modules and differential genes. Functional enrichment analysis was conducted to unveil the involvement of potential pathways. To identify key biomarkers for renal fibrosis, we utilized logistic analysis, a LASSO-based tenfold cross-validation approach, and gene topological analysis within Cytoscape. Furthermore, histological staining, Western blotting (WB), and quantitative PCR (qPCR) experiments were performed in a murine model of renal fibrosis to verify the identified hub genes. Moreover, molecular docking and molecular dynamics simulations were conducted to explore possible effective drugs. RESULTS Through WGCNA, the intersection of core modules and differential genes yielded a compendium of 92 potential genes. Logistic analysis, LASSO-based tenfold cross-validation, and gene topological analysis within Cytoscape identified four core genes (CD3G, CORO1A, FCGR2A, and GZMH) associated with renal fibrosis. The expression of these core genes was confirmed through single-cell data analysis and validated using various machine learning methods. Wet experiments also verified the upregulation of these core genes in the murine model of renal fibrosis. A positive correlation was observed between the core genes and immune cells, suggesting their potential role in bolstering immune system activity. Moreover, four potentially effective small molecules (ZINC000003830276-Tessalon, ZINC000003944422-Norvir, ZINC000008214629-Nonoxynol-9, and ZINC000085537014-Cobicistat) were identified through molecular docking and molecular dynamics simulations. CONCLUSION Four potential hub biomarkers most associated with post-transplant renal fibrosis, as well as four potentially effective small molecules, were identified, providing valuable insights for studying the molecular mechanisms underlying post-transplant renal fibrosis and exploring new targets.
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Affiliation(s)
- Xinhao Niu
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Shanghai, 200032, China
| | - Cuidi Xu
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Shanghai, 200032, China
| | - Yin Celeste Cheuk
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Shanghai, 200032, China
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Xiaoqing Xu
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Shanghai, 200032, China
| | - Lifei Liang
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Shanghai, 200032, China
| | - Pingbao Zhang
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Shanghai, 200032, China
| | - Ruiming Rong
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Key Laboratory of Organ Transplantation, Shanghai, 200032, China.
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Han S, Lee JE, Kang S, So M, Jin H, Lee JH, Baek S, Jun H, Kim TY, Lee YS. Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. Brief Bioinform 2024; 25:bbae035. [PMID: 38349059 PMCID: PMC10862655 DOI: 10.1093/bib/bbae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.
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Affiliation(s)
- Seokjin Han
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Ji Eun Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Seolhee Kang
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Minyoung So
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hee Jin
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Jang Ho Lee
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Sunghyeob Baek
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hyungjin Jun
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Tae Yong Kim
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Yun-Sil Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
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Pillai M, Wu D. Validation approaches for computational drug repurposing: a review. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:559-568. [PMID: 38222367 PMCID: PMC10785886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Affiliation(s)
- Malvika Pillai
- Stanford University, Stanford, CA
- University of North Carolina, Chapel Hill, NC
| | - Di Wu
- University of North Carolina, Chapel Hill, NC
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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573505. [PMID: 38234732 PMCID: PMC10793435 DOI: 10.1101/2023.12.28.573505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
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Pelissier A, Laragione T, Harris C, Martínez MR, Gulko PS. Gene Network Analyses Identify Co-regulated Transcription Factors and BACH1 as a Key Driver in Rheumatoid Arthritis Fibroblast-like Synoviocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573506. [PMID: 38234777 PMCID: PMC10793426 DOI: 10.1101/2023.12.28.573506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying Rheumatoid Arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks (GRNs) to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This is the first time that BACH1 is shown to have a central role in the regulation of FLS phenotypes, and gene expression signatures, as well as in ferroptosis and fatty acid metabolism. These new discoveries have the potential to become new targets for treatments aimed at selectively targeting the RA FLS.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Carolyn Harris
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
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Fan B, Cheng C, Yang Y, Wang P, Xia H, Wu M, Li H, Manzoor Syed B, Liu Q. Construction of an adverse outcome pathway framework based on integrated data to evaluate arsenic-induced non-alcoholic fatty liver disease. ENVIRONMENT INTERNATIONAL 2024; 183:108381. [PMID: 38118209 DOI: 10.1016/j.envint.2023.108381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/30/2023] [Accepted: 12/10/2023] [Indexed: 12/22/2023]
Abstract
Arsenic is a recognized environmental pollutant naturally occurring in aquifers through geological processes. Toxicological studies have revealed that liver is the main target organ harmed by arsenic exposure. However, systematic studies of non-alcoholic fatty liver disease (NAFLD) are not comprehensive, and information regarding threats and risk assessment remains insufficient. This research aimed to examine the association between arsenic exposure and NAFLD and uncover the role of molecular initiating events and key events in disease development using the Adverse Outcome Pathway (AOP). Data from 8,104 adults in the National Health and Nutrition Examination Survey were used to explore the relationship between urinary arsenic and NAFLD. In a logistic regression model, urinary inorganic arsenic levels positively correlated with NAFLD (odds ratio = 1.12, 95 % confidence interval = 1.07-1.16). Subsequently, to gain a deeper understanding of arsenic-induced NAFLD, an AOP framework was constructed, revealing that arsenic exposure led to elevate levels of TNF-α, which regulated the NF-κB pathway and led to hepatic lipid deposition, causing NAFLD. This AOP was assessed as "high" according to the Organization for Economic Co-operation and Development users' handbook, and in vitro and in vivo models validated the AOP framework. In summary, this study highlights the potential mechanisms of arsenic-induced NAFLD. We combined the AOP with classical toxicological approaches with a view of establishing, rapidly and accurately, the lowest level at which environmental arsenic exposure can have adverse effects on the body, thereby contributing to risk assessment strategies for arsenic exposure through iterative and animal modeling at the population level.
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Affiliation(s)
- Bowen Fan
- Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Suzhou Institute of Public Health, Gusu School, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China
| | - Cheng Cheng
- Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Suzhou Institute of Public Health, Gusu School, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China
| | - Yi Yang
- Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Suzhou Institute of Public Health, Gusu School, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China
| | - Peiwen Wang
- Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Suzhou Institute of Public Health, Gusu School, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China
| | - Haibo Xia
- Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Suzhou Institute of Public Health, Gusu School, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China
| | - Meng Wu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, Jiangsu, People's Republic of China
| | - Han Li
- Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Suzhou Institute of Public Health, Gusu School, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China
| | - Binafsha Manzoor Syed
- Medical Research Centre, Liaquat University of Medical & Health Sciences, Jamshoro 76090, Sindh, Pakistan.
| | - Qizhan Liu
- Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Suzhou Institute of Public Health, Gusu School, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People's Republic of China.
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Lou Y, Zhu X, Tan K. Dictionary-based matching graph network for biomedical named entity recognition. Sci Rep 2023; 13:21667. [PMID: 38066007 PMCID: PMC10709457 DOI: 10.1038/s41598-023-48564-w] [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: 06/25/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER. Biomedical dictionaries, implemented through a masked manner, are frequently employed in these methods to enhance entity recognition. However, their performance remains limited. In this work, we propose a dictionary-based matching graph network for BioNER. This approach utilizes the matching graph method to project all possible dictionary-based entity combinations in the text onto a directional graph. The network is implemented coherently with a bi-directional graph convolutional network (BiGCN) that incorporates the matching graph information. Our proposed approach fully leverages the dictionary-based matching graph instead of a simple masked manner. We have conducted numerous experiments on five typical Bio-NER datasets. The proposed model shows significant improvements in F1 score compared to the state-of-the-art (SOTA) models: 2.8% on BC2GM, 1.3% on BC4CHEMD, 1.1% on BC5CDR, 1.6% on NCBI-disease, and 0.5% on JNLPBA. The results show that our model, which is superior to other models, can effectively recognize natural biomedical named entities.
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Affiliation(s)
- Yinxia Lou
- School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China
| | - Xun Zhu
- School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China.
| | - Kai Tan
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
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Stojilković N, Radović B, Vukelić D, Ćurčić M, Antonijević Miljaković E, Buha Đorđević A, Baralić K, Marić Đ, Bulat Z, Đukić-Ćosić D, Antonijević B. Involvement of toxic metals and PCBs mixture in the thyroid and male reproductive toxicity: In silico toxicogenomic data mining. ENVIRONMENTAL RESEARCH 2023; 238:117274. [PMID: 37797666 DOI: 10.1016/j.envres.2023.117274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/07/2023]
Abstract
Toxicological research is mostly limited to considering the effects of a single substance, even though the real exposure of people is reflected in their daily exposure to many different chemical substances in low-doses. This in silico toxicogenomic study aims to provide evidence for the selected environmental (organo)metals (lead, cadmium, methyl mercury) + polychlorinated biphenyls mixture involvement in the possible alteration of thyroid, and male reproductive system function, and furthermore to predict the possible toxic mechanisms of the environmental cocktail. The Comparative Toxicogenomic Database, GeneMANIA online software, and ToppGene Suite portal were used as the main tools for toxicogenomic data mining and gene ontology analysis. The results show that 35 annotated common genes between selected chemicals and endocrine system diseases can interact on the co-expression level. Our study highlighted the disruption of the cytokines, the cell's response to oxidative stress, and the influence of the transcription factors as the potential core of toxicological mechanisms of the discussed mixture's effects. The connected toxicological effects of the tested mixture were abnormal sperm cells, a disrupted level of testosterone, and thyroid hormones. The core mechanisms of these effects were inflammation, oxidative stress, disruption of androgen receptor signaling, and the alteration of the FOXO3-Keap-1/NRF2-HMOX1-NQO1 pathway signaling most likely controlled by the co-expression of overlapped genes among used chemicals. This in silico research can be used as a potential core for the determination of biomarkers that can be monitored in future further in vitro and in vivo experiments.
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Affiliation(s)
- Nikola Stojilković
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Biljana Radović
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Dragana Vukelić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Marijana Ćurčić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia.
| | - Evica Antonijević Miljaković
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Aleksandra Buha Đorđević
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Katarina Baralić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Đurđica Marić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Zorica Bulat
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Danijela Đukić-Ćosić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Biljana Antonijević
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
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Tan M, Wu D, Zhou Y, Duan B. Centella triterpenes cream as a potential drug for the treatment of hypertrophic scar through inhibiting the phosphorylation of STAT3: A network pharmacology analysis and in vitro experiments. J Cosmet Dermatol 2023; 22:3511-3519. [PMID: 37563868 DOI: 10.1111/jocd.15883] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/18/2023] [Accepted: 06/13/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Hypertrophic scars (HS) often affect the normal function and appearance of the skin and bring adverse effects to the body and mind of patients, being a challenge in the fields of burns and plastic surgery as well as rehabilitation. Despite significant efficacy of centella triterpenes cream for treating HS clinically, its pharmacodynamics and molecular targets are still unclear. Therefore, the network pharmacology analysis combined with in vitro cell molecular biology experiments was used to explore the mechanism and targets of centella triterpenes cream treating HS in this study. METHODS First, target genes of asiaticoside (AC) were obtained from the databases including the Comparative Toxicogenomics Database, similarity ensemble approach, SwissTargetPrediction and TargetNet, and HS targets were acquired from the databases like Disgenet, GeneCards, and Online Mendelian Inheritance in Man. The common targets of AC-HS were obtained through plotting a Venn diagram. Subsequently, STRING 11.0 was employed for analyzing the protein-protein interaction (PPI) network of the common targets, and cytoscape 3.9.0 for analyzing the connectivity of PPI and plotting the network diagram of "drug-component-target". Additionally, a modified tissue culture method was applied to separate primary normal fibroblasts (NFs) in human skin and hypertrophic scar fibroblasts (HSFs). HSFs after 24-h AC treatment were subjected to MTT assay to detect cell viability, scratch assay to assess cell migration ability, and western blot to test the protein expression levels of STAT3, p-STAT3, transforming growth factor-β1 (TGF-β1), collagen I (COL 1), fibronectin 1 (FN1), and alpha-smooth muscle actin (α-SMA). RESULTS In network pharmacology analysis, 134 pharmacodynamic targets of AC and 2333 HS targets were obtained after retrieving the database, 50 AC-HS common targets were obtained by a Venn diagram, and a total of 178 edges and 13 core genes such as JUN and STAT3 were acquired by PPI analysis. In vitro experiments showed that the phosphorylation level of STAT3 (p-STAT3) was increased in HSFs. In addition to reducing p-STAT3 in HSFs, AC significantly inhibited the cell viability and migration of HSFs and downregulated the protein levels of TGF-β1, COL 1, FN 1, and α-SMA. CONCLUSION STAT3 can be activated in HS. AC may exert its pharmacological effects of inhibiting TGF-β1 signal transduction and regulating extracellular matrix remodeling in HS by inhibiting STAT3 phosphorylation. However, the specific molecular mechanism of AC remains to be verified through further experiments.
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Affiliation(s)
- Ming Tan
- Department of Plastic and Cosmetic Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Wu
- Department of Plastic and Cosmetic Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanshijing Zhou
- Department of Plastic and Cosmetic Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Duan
- Department of Plastic and Cosmetic Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Kartchner D, Deng J, Lohiya S, Kopparthi T, Bathala P, Domingo-Fernández D, Mitchell CS. A Comprehensive Evaluation of Biomedical Entity Linking Models. PROCEEDINGS OF THE CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING. CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING 2023; 2023:14462-14478. [PMID: 38756862 PMCID: PMC11097978 DOI: 10.18653/v1/2023.emnlp-main.893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking.
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Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). MEDICAL REVIEW (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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Hu Y, Chen Y, Qin Y, Huang R. Learning entity-oriented representation for biomedical relation extraction. J Biomed Inform 2023; 147:104527. [PMID: 37852347 DOI: 10.1016/j.jbi.2023.104527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 10/11/2023] [Accepted: 10/15/2023] [Indexed: 10/20/2023]
Abstract
Biomedical Relation Extraction (BioRE) aims to automatically extract semantic relations for given entity pairs and is of great significance in biomedical research. Current popular methods often utilize pretrained language models to extract semantic features from individual input instances, which frequently suffer from overlapping semantics. Overlapping semantics refers to the situation in which a sentence contains multiple entity pairs that share the same context, leading to highly similar information between these entity pairs. In this study, we propose a model for learning Entity-oriented Representation (EoR) that aims to improve the performance of the model by enhancing the discriminability between entity pairs. It contains three modules: sentence representation, entity-oriented representation, and output. The first module learns the global semantic information of the input instance; the second module focuses on extracting the semantic information of the sentence from the target entities; and the third module enhances distinguishability among entity pairs and classifies the relation type. We evaluated our approach on four BioRE tasks with eight datasets, and the experiments showed that our EoR achieved state-of-the-art performance for PPI, DDI, CPI, and DPI tasks. Further analysis demonstrated the benefits of entity-oriented semantic information in handling multiple entity pairs in the BioRE task.
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Affiliation(s)
- Ying Hu
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Yanping Chen
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Yongbin Qin
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Ruizhang Huang
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
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Zhou L, Tan J, Dai Y, Zhu K, Xiao Y, Wu D, Wang Z, Tan Y, Qin Y. Jiawei Danxuan Koukang Alleviates Arecoline Induced Oral Mucosal Lesions: Network Pharmacology and the Combined Ultra-High Performance Liquid Chromatography (UPLC) and Mass Spectrometry (MS). Drug Des Devel Ther 2023; 17:3085-3101. [PMID: 37854130 PMCID: PMC10581390 DOI: 10.2147/dddt.s413897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/29/2023] [Indexed: 10/20/2023] Open
Abstract
Purpose Arecoline is one of the main toxic components of arecoline to cause oral mucosal lesions or canceration, which seriously affects the survival and life quality of patients. This study analyzed the mechanism of Jiawei Danxuan Koukang (JDK) in alleviating arecoline induced oral mucosal lesions, to provide new insights for the treatment of oral submucosal fibrosis (OSF) or cancerosis. Methods Metabolomics was applied to analyze the composition of JDK and serum metabolites. The active ingredients of JDK were analyzed by the combined ultra-high performance liquid chromatography and mass spectrometry. The target network of JDK, metabolites and OSF was analyzed by network pharmacology, and molecular docking. Oral mucosal lesions and fibrosis were analyzed by HE and Masson staining. Cell differentiation, proliferation and apoptosis were detected. The expressions of α-SMA, Collagen I, Vimentin, Snail, E-cadherin, AR and NOTCH1 were detected by Western blot. Results Arecoline induced the gradual atrophy and thinning of rat oral mucosal, collagen accumulation, the increase expressions of fibrosis-related proteins and Th17/Treg ratio. JDK inhibited arecoline-induced oral mucosal lesions and inflammatory infiltration. Arecoline induced changes of serum metabolites in Aminoacyl-tRNA biosynthesis, Alanine, aspartate and glutamate metabolism and Arginine biosynthesis pathways, which were reversed by M-JDK. Quercetin and AR were the active ingredients and key targets of JDK, metabolites and OSF interaction. Arecoline promoted the expression of AR protein, and the proliferation of oral fibroblasts. Quercetin inhibited the effect of arecoline on oral fibroblasts, but was reversed by AR overexpression. Arecoline induced NOTCH1 expression in CAL27 and SCC-25 cells, and promoted cell proliferation, but was reversed by M-JDK or quercetin. Conclusion JDK improved the arecoline-induced OSF and serum metabolite functional pathway. Quercetin targeted AR protein to improve arecoline-induced OSF. JDK and quercetin inhibited arecoline-induced NOTCH1 protein expression in CAL27 and SCC-25 cells to play an anti-oral cancer role.
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Affiliation(s)
- Linghang Zhou
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Jin Tan
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Yuzhe Dai
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Keke Zhu
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Yanbo Xiao
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Dan Wu
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Zongkang Wang
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Yisi Tan
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
| | - Yijie Qin
- Department of Stomatology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People’s Republic of China
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Wang X, Li F, Meng X, Xia C, Ji C, Wu H. Abnormality of mussel in the early developmental stages induced by graphene and triphenyl phosphate: In silico toxicogenomic data-mining, in vivo, and toxicity pathway-oriented approach. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 263:106674. [PMID: 37666107 DOI: 10.1016/j.aquatox.2023.106674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/25/2023] [Accepted: 08/27/2023] [Indexed: 09/06/2023]
Abstract
Increasing number of complex mixtures of organic pollutants in coastal area (especially for nanomaterials and micro/nanoplastics associated chemicals) threaten aquatic ecosystems and their joint hazards are complex and demanding tasks. Mussels are the most sensitive marine faunal groups in the world, and their early developmental stages (embryo and larvae) are particularly susceptible to environmental contaminants, which can distinguish the probable mechanisms of mixture-induced growth toxicity. In this study, the potential critical target and biological processes affected by graphene and triphenyl phosphate (TPP) were developed by mining public toxicogenomic data. And their combined toxic effects were verified by toxicological assay at early developmental stages in filter-feeding mussels (embryo and larvae). It showed that interactions among graphene/TPP with 111 genes (ABCB1, TP53, SOD, CAT, HSP, etc.) affected phenotypes along conceptual framework linking these chemicals to developmental abnormality endpoints. The PPAR signaling pathway, monocarboxylic acid metabolic process, regulation of lipid metabolic process, response to oxidative stress, and gonad development were noted as the key molecular pathways that contributed to the developmental abnormality. Enriched phenotype analysis revealed biological processes (cell proliferation, cell apoptosis, inflammatory response, response to oxidative stress, and lipid metabolism) affected by the investigated mixture. Combined, our results supported that adverse effects induced by contaminants/ mixture could not only be mediated by single receptor signaling or be predicted by the simple additive effect of contaminants. The results offer a framework for better comprehending the developmental toxicity of environmental contaminants in mussels and other invertebrate species, which have considerable potential for hazard assessment of coastal mixture.
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Affiliation(s)
- Xiaoqing Wang
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Fei Li
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China.
| | - Xiangjing Meng
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Chunlei Xia
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
| | - Chenglong Ji
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
| | - Huifeng Wu
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
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Mobeen SA, Saxena P, Jain AK, Deval R, Riazunnisa K, Pradhan D. Integrated bioinformatics approach to unwind key genes and pathways involved in colorectal cancer. J Cancer Res Ther 2023; 19:1766-1774. [PMID: 38376276 DOI: 10.4103/jcrt.jcrt_620_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 12/13/2021] [Indexed: 02/21/2024]
Abstract
BACKGROUND Colorectal cancer (CRC) is the fifth leading cause of death in India. Until now, the exact pathogenesis concerning CRC signaling pathways is largely unknown; however, the diseased condition is believed to deteriorate with lifestyle, aging, and inherited genetic disorders. Hence, the identification of hub genes and therapeutic targets is of great importance for disease monitoring. OBJECTIVE Identification of hub genes and targets for identification of candidate hub genes for CRC diagnosis and monitoring. MATERIALS AND METHODS The present study applied gene expression analysis by integrating two profile datasets (GSE20916 and GSE33113) from NCBI-GEO database to elucidate the potential key candidate genes and pathways in CRC. Differentially expressed genes (DEGs) between CRC (195 CRC tissues) and healthy control (46 normal mucosal tissue) were sorted using GEO2R tool. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using Cluster Profiler in Rv. 3.6.1. Moreover, protein-protein interactions (PPI), module detection, and hub gene identification were accomplished and visualized through the Search Tool for the Retrieval of Interacting Genes, Molecular Complex Detection (MCODE) plug-in of Cytoscape v3.8.0. Further hub genes were imported into ToppGene webserver for pathway analysis and prognostic expression analysis was conducted using Gene Expression Profiling Interactive Analysis webserver. RESULTS A total of 2221 DEGs, including 1286 up-regulated and 935down-regulated genes mainly enriched in signaling pathways of NOD-like receptor, FoxO, AMPK signalling and leishmaniasis. Three key modules were detected from PPI network using MCODE. Besides, top 20 high prioritized hub genes were selected. Further, prognostic expression analysis revealed ten of the hub genes, namely IL1B, CD44, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, MMP9, CREB1, STAT1, vascular endothelial growth factor (VEGFA), CDC5 L, Ataxia-telangiectasia mutated (ATM + and CDH1 to be differently expressed in normal and cancer patients. CONCLUSION The present study proposed five novel therapeutic targets, i.e., ATM, GAPDH, CREB1, VEGFA, and CDH1 genes that might provide new insights into molecular oncogenesis of CRC.
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Affiliation(s)
- Syeda Anjum Mobeen
- Department of Biotechnology and Bioinformatics, Yogi Vemana University, Andhra Pradesh, India
| | - Pallavi Saxena
- Biomedical Informatics Centre, Indian Council of Medical Research, National Institute of Pathology, New Delhi, India
- Department of Biotechnology, Invertis University, Bareilly, Uttar Pradesh, India
| | - Arun Kumar Jain
- Biomedical Informatics Centre, Indian Council of Medical Research, National Institute of Pathology, New Delhi, India
| | - Ravi Deval
- Department of Biotechnology, Invertis University, Bareilly, Uttar Pradesh, India
| | - Khateef Riazunnisa
- Department of Biotechnology and Bioinformatics, Yogi Vemana University, Andhra Pradesh, India
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Sourati J, Evans JA. Accelerating science with human-aware artificial intelligence. Nat Hum Behav 2023; 7:1682-1696. [PMID: 37443269 DOI: 10.1038/s41562-023-01648-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 06/02/2023] [Indexed: 07/15/2023]
Abstract
Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences that are cognitively accessible to experts dramatically improves (by up to 400%) AI prediction of future discoveries beyond models focused on research content alone, especially when relevant literature is sparse. These models succeed by predicting human predictions and the scientists who will make them. By tuning human-aware AI to avoid the crowd, we can generate scientifically promising 'alien' hypotheses unlikely to be imagined or pursued without intervention until the distant future, which hold promise to punctuate scientific advance beyond questions currently pursued. By accelerating human discovery or probing its blind spots, human-aware AI enables us to move towards and beyond the contemporary scientific frontier.
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Affiliation(s)
- Jamshid Sourati
- Department of Sociology, University of Chicago, Chicago, IL, USA
| | - James A Evans
- Department of Sociology, University of Chicago, Chicago, IL, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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Umar AH, Ratnadewi D, Rafi M, Sulistyaningsih YC, Hamim H, Kusuma WA. Drug candidates and potential targets of Curculigo spp. compounds for treating diabetes mellitus based on network pharmacology, molecular docking and molecular dynamics simulation. J Biomol Struct Dyn 2023; 41:8544-8560. [PMID: 36300505 DOI: 10.1080/07391102.2022.2135597] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/08/2022] [Indexed: 10/31/2022]
Abstract
Curculigo spp. is a herb that is commonly used in Indonesia to treat diabetes mellitus (DM) . The main active components of Curculigo spp. were identified through our previous metabolomic study and online database platform. However, the biological mechanisms underlying Curculigo spp. activity in treating DM remain unclear. Therefore, in this study, a network pharmacology was used to explore the active compounds of Curculigo spp. and their potential molecular mechanisms for treating DM. Oral bioavailability and drug-likeness from the compounds of Curculigo spp. were screened using Lipinski's rule of five, BBB, HIA + and Caco-2 permeability criteria. A network of compound-target-disease-pathway was then constructed using Cytoscape. The highest degree compounds and targets were then confirmed by molecular docking and molecular dynamics (MD) simulations. The human body can absorb 33 compounds derived from Curculigo spp. In addition, 58 nodes and 62 edges generated a network analysis with the DM target. The highest degree of the compound-target-disease pathway was for orcinol glucoside, AKR1B1, autoimmune diabetes, bile acid and bile salt metabolism. Furthermore, the computational docking method on Curculigo spp. compounds with the highest degree revealed that orcinol glucoside interacted with PTPN1 through a hydrogen bond and resulted in a binding energy of -7.2 kcal mol-1. Through hydrogen bonds, orcinol glucoside in PTPN1 regulates multiple signaling pathways via the adherens junction pathway, which may play a therapeutic role in DM (type 2 diabetes: obesity). In addition, MD simulation confirmed that orcinol glucoside, is suitable for DM treatment by interacting with PTPN1.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abdul Halim Umar
- Division of Pharmaceutical Biology, College of Pharmaceutical Sciences Makassar (Sekolah Tinggi Ilmu Farmasi Makassar), Makassar, Indonesia
| | - Diah Ratnadewi
- Department of Biology, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | - Mohamad Rafi
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | | | - Hamim Hamim
- Department of Biology, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | - Wisnu Ananta Kusuma
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
- Tropical Biopharmaca Research Center, IPB University, Bogor, Indonesia
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Dixit N, Motwani H, Patel SK, Rawal RM, Solanki HA. Decoding the mechanism of andrographolide to combat hepatocellular carcinoma: a network pharmacology integrated molecular docking and dynamics approach. J Biomol Struct Dyn 2023:1-19. [PMID: 37728545 DOI: 10.1080/07391102.2023.2256866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/02/2023] [Indexed: 09/21/2023]
Abstract
HepatoCellular Carcinoma, being one of the most mortally convoluted malignancy with mounting number of occurrences across the world and being classified as the third most prevalent cause of cancer-associated mortalities and sixth most prevalent neoplasia. The active phytoconstituent andrographolide, derived from Andrographis paniculata is conveyed to reconcile a number of human ailments including various oncologies. However, the molecular mechanism underlying the anti-oncogenic effects of Andrographolide on HCC remains skeptical and unclear, emerging as a budding challenge for researchers and oncologists. The present study intends to analyze the underlying pharmacological mechanism of Andrographolide over HCC, established via assimilated approach of network pharmacology. Herein, the Network pharmacology stratagem was instigated to investigate potential HCC targets. The Andrographolide targets along with HCC targets were extracted from multiple databases. A total of 162 potential overlapping targets among HCC and Andrographolide were obtained and further subjected to gene ontology and Pathway enrichment analysis by employing OmicsBox and DAVID database, respectively. Subsequently, Protein-protein interaction network construction by Cytoscape software identified the top 10 hub nodes which were validated by survival and expression analysis. Further, the results derived from molecular docking and dynamic simulations by CB-Dock2 server and Desmond module (Schrodinger software) indicate ALB, CCND1, HIF1A, TNF, and VEGFA as potential Andrographolide related targets with high binding affinity and promising complex stability. Our findings not only reveal the antioncogenic role of andrographolide but also provide novel insights illuminating the identified targets as scientific foundation for anti-oncogenic clinical application of andrographolide in HCC therapeutics.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Nandan Dixit
- Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, Gujarat, India
| | - Harsha Motwani
- Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, Gujarat, India
| | - Saumya K Patel
- Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, Gujarat, India
| | - Rakesh M Rawal
- Department of Life Science, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India
| | - Hiteshkumar A Solanki
- Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, Gujarat, India
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Yang M, Yang B, Duan G, Wang J. ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis. Front Genet 2023; 14:1271311. [PMID: 37795241 PMCID: PMC10545866 DOI: 10.3389/fgene.2023.1271311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/23/2023] [Indexed: 10/06/2023] Open
Abstract
Background: Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications. Methods: In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug-disease associations. First, we use a weighted k-nearest neighbor (WKNN) approach to increase the overall density of the drug-disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug-disease association pairs derived from the low-rank drug and low-rank disease tensors. Results: We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method.
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Affiliation(s)
- Mengyun Yang
- School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China
- School of Computer Science, Hunan First Normal University, Changsha, China
| | - Bin Yang
- School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, China
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Xu W, Duan L, Zheng H, Li-Ling J, Jiang W, Zhang Y, Wang T, Qin R. An Integrative Disease Information Network Approach to Similar Disease Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2724-2735. [PMID: 34478379 DOI: 10.1109/tcbb.2021.3110127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g., disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrative approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established first. Then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.
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Ai C, Yang H, Ding Y, Tang J, Guo F. Low Rank Matrix Factorization Algorithm Based on Multi-Graph Regularization for Detecting Drug-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3033-3043. [PMID: 37159322 DOI: 10.1109/tcbb.2023.3274587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Detecting potential associations between drugs and diseases plays an indispensable role in drug development, which has also become a research hotspot in recent years. Compared with traditional methods, some computational approaches have the advantages of fast speed and low cost, which greatly accelerate the progress of predicting the drug-disease association. In this study, we propose a novel similarity-based method of low-rank matrix decomposition based on multi-graph regularization. On the basis of low-rank matrix factorization with L2 regularization, the multi-graph regularization constraint is constructed by combining a variety of similarity matrices from drugs and diseases respectively. In the experiments, we analyze the difference in the combination of different similarities, resulting that combining all the similarity information on drug space is unnecessary, and only a part of the similarity information can achieve the desired performance. Then our method is compared with other existing models on three data sets (Fdataset, Cdataset and LRSSLdataset) and have a good advantage in the evaluation measurement of AUPR. Besides, a case study experiment is conducted and showing that the superior ability for predicting the potential disease-related drugs of our model. Finally, we compare our model with some methods on six real world datasets, and our model has a good performance in detecting real world data.
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50
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Law J, Orbach SM, Weston BR, Steele PA, Rajagopalan P, Murali TM. Computational Construction of Toxicant Signaling Networks. Chem Res Toxicol 2023; 36:1267-1277. [PMID: 37471124 PMCID: PMC10445288 DOI: 10.1021/acs.chemrestox.2c00422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Indexed: 07/21/2023]
Abstract
Humans and animals are regularly exposed to compounds that may have adverse effects on health. The Toxicity Forecaster (ToxCast) program was developed to use high throughput screening assays to quickly screen chemicals by measuring their effects on many biological end points. Many of these assays test for effects on cellular receptors and transcription factors (TFs), under the assumption that a toxicant may perturb normal signaling pathways in the cell. We hypothesized that we could reconstruct the intermediate proteins in these pathways that may be directly or indirectly affected by the toxicant, potentially revealing important physiological processes not yet tested for many chemicals. We integrate data from ToxCast with a human protein interactome to build toxicant signaling networks that contain physical and signaling protein interactions that may be affected as a result of toxicant exposure. To build these networks, we developed the EdgeLinker algorithm, which efficiently finds short paths in the interactome that connect the receptors to TFs for each toxicant. We performed multiple evaluations and found evidence suggesting that these signaling networks capture biologically relevant effects of toxicants. To aid in dissemination and interpretation, interactive visualizations of these networks are available at http://graphspace.org.
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Affiliation(s)
- Jeffrey
N. Law
- Interdisciplinary
Ph.D. Program in Genetics, Bioinformatics, and Computational Biology, Blacksburg, Virginia 24061, United States
| | - Sophia M. Orbach
- Department
of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Bronson R. Weston
- Interdisciplinary
Ph.D. Program in Genetics, Bioinformatics, and Computational Biology, Blacksburg, Virginia 24061, United States
| | - Peter A. Steele
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Padmavathy Rajagopalan
- Department
of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - T. M. Murali
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
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