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Asad M, Hassan A, Wang W, Alonazi WB, Khan MS, Ogunyemi SO, Ibrahim M, Bin L. An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology. Comput Biol Med 2024; 178:108738. [PMID: 38870724 DOI: 10.1016/j.compbiomed.2024.108738] [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: 01/25/2024] [Revised: 05/15/2024] [Accepted: 06/08/2024] [Indexed: 06/15/2024]
Abstract
Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.
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Affiliation(s)
- Muhammad Asad
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan
| | - Ahmad Hassan
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan
| | - Weiyu Wang
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Wadi B Alonazi
- Health Administration Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | | | - Solabomi Olaitan Ogunyemi
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Muhammad Ibrahim
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan.
| | - Li Bin
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
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2
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Kashobwe L, Sadrabadi F, Brunken L, Coelho ACMF, Sandanger TM, Braeuning A, Buhrke T, Öberg M, Hamers T, Leonards PEG. Legacy and alternative per- and polyfluoroalkyl substances (PFAS) alter the lipid profile of HepaRG cells. Toxicology 2024:153862. [PMID: 38866127 DOI: 10.1016/j.tox.2024.153862] [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: 03/05/2024] [Revised: 06/07/2024] [Accepted: 06/08/2024] [Indexed: 06/14/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals used in various industrial and consumer products. They have gained attention due to their ubiquitous occurrence in the environment and potential for adverse effects on human health, often linked to immune suppression, hepatotoxicity, and altered cholesterol metabolism. This study aimed to explore the impact of ten individual PFAS, 3H-perfluoro-3-[(3-methoxypropoxy) propanoic acid] (PMPP/Adona), ammonium perfluoro-(2-methyl-3-oxahexanoate) (HFPO-DA/GenX), perfluorobutanoic acid (PFBA), perfluorobutanesulfonic acid (PFBS), perfluorodecanoic acid (PFDA), perfluorohexanoic acid (PFHxA), perfluorohexanesulfonate (PFHxS), perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS) on the lipid metabolism in human hepatocyte-like cells (HepaRG). These cells were exposed to different concentrations of PFAS ranging from 10µM to 5000µM. Lipids were extracted and analyzed using liquid chromatography coupled with mass spectrometry (LC- MS-QTOF). PFOS at 10µM and PFOA at 25µM increased the levels of ceramide (Cer), diacylglycerol (DAG), N-acylethanolamine (NAE), phosphatidylcholine (PC), and triacylglycerol (TAG) lipids, while PMPP/Adona, HFPO-DA/GenX, PFBA, PFBS, PFHxA, and PFHxS decreased the levels of these lipids. Furthermore, PFOA and PFOS markedly reduced the levels of palmitic acid (FA 16.0). The present study shows distinct concentration-dependent effects of PFAS on various lipid species, shedding light on the implications of PFAS for essential cellular functions. Our study revealed that the investigated legacy PFAS (PFOS, PFOA, PFBA, PFDA, PFHxA, PFHxS, and PFNA) and alternative PFAS (PMPP/Adona, HFPO-DA/GenX and PFBS) can potentially disrupt lipid homeostasis and metabolism in hepatic cells. This research offers a comprehensive insight into the impacts of legacy and alternative PFAS on lipid composition in HepaRG cells.
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Affiliation(s)
- Lackson Kashobwe
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment, De Boelelaan 1105, Amsterdam, Netherlands
| | - Faezeh Sadrabadi
- Department of Food Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Lars Brunken
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Ana Carolina M F Coelho
- Department of Community Medicine, Faculty of Health Sciences, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Torkjel M Sandanger
- Department of Community Medicine, Faculty of Health Sciences, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Albert Braeuning
- Department of Food Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Thorsten Buhrke
- Department of Food Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Mattias Öberg
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Timo Hamers
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment, De Boelelaan 1105, Amsterdam, Netherlands
| | - Pim E G Leonards
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment, De Boelelaan 1105, Amsterdam, Netherlands.
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3
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Islam MA, Majumder MZH, Miah MS, Jannaty S. Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction. Comput Biol Med 2024; 176:108432. [PMID: 38744014 DOI: 10.1016/j.compbiomed.2024.108432] [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: 02/08/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 05/16/2024]
Abstract
This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection-play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.
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Affiliation(s)
- Md Ariful Islam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
| | | | - Md Sohel Miah
- Department of Computer Science and Technology, Moulvibazar Polytechnic Institute, Bangladesh
| | - Sumaia Jannaty
- Gonoshasthaya Samaj Vittik Medical College, Savar, Dhaka, Bangladesh
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4
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Fu Y, Wang C, Wu Z, Zhang X, Liu Y, Wang X, Liu F, Chen Y, Zhang Y, Zhao H, Wang Q. Discovery of the potential biomarkers for early diagnosis of endometrial cancer via integrating metabolomics and transcriptomics. Comput Biol Med 2024; 173:108327. [PMID: 38552279 DOI: 10.1016/j.compbiomed.2024.108327] [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: 02/06/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Endometrial cancer (EC) is one of the most common malignant tumors in women, and the increasing incidence and mortality pose a serious threat to the public health. Early diagnosis of EC could prolong the survival period and optimize the survivorship, greatly alleviating patients' suffering and social medical pressure. In this study, we collected urine and serum samples from the recruited patients, analyzed the samples using LC-MS approach, and identified the differential metabolites through metabolomic analysis. Then, the differentially expressed genes were identified through the systematic transcriptomic analysis of EC-related dataset from Gene Expression Omnibus (GEO), followed by network profiling of metabolic-reaction-enzyme-gene. In this experiment, a total of 83 differential metabolites and 19 hub genes were discovered, of which 10 different metabolites and 3 hub genes were further evaluated as more potential biomarkers based on network analysis. According to the KEGG enrichment analysis, the potential biomarkers and gene-encoded proteins were found to be involved in the arginine and proline metabolism, histidine metabolism, and pyrimidine metabolism, which was of significance for the early diagnosis of EC. In particular, the combination of metabolites (histamine, 1-methylhistamine, and methylimidazole acetaldehyde) as well as the combination of RRM2, TYMS and TK1 exerted more accurate discrimination abilities between EC and healthy groups, providing more criteria for the early diagnosis of EC.
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Affiliation(s)
- Yan Fu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China
| | - Chengzhao Wang
- College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Zhimin Wu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xiaoguang Zhang
- Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China; College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yan Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xu Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Fangfang Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yujuan Chen
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
| | - Huanhuan Zhao
- Department of Obstetrics and Gynecology, The Fourth Hospital of Hebei Medical University, 050011, China.
| | - Qiao Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
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Zhang W, Mou M, Hu W, Lu M, Zhang H, Zhang H, Luo Y, Xu H, Tao L, Dai H, Gao J, Zhu F. MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery. J Chem Inf Model 2024; 64:2720-2732. [PMID: 38373720 DOI: 10.1021/acs.jcim.4c00013] [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: 02/21/2024]
Abstract
In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.
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Affiliation(s)
- Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wei Hu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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6
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Viant MR, Amstalden E, Athersuch T, Bouhifd M, Camuzeaux S, Crizer DM, Driemert P, Ebbels T, Ekman D, Flick B, Giri V, Gómez-Romero M, Haake V, Herold M, Kende A, Lai F, Leonards PEG, Lim PP, Lloyd GR, Mosley J, Namini C, Rice JR, Romano S, Sands C, Smith MJ, Sobanski T, Southam AD, Swindale L, van Ravenzwaay B, Walk T, Weber RJM, Zickgraf FM, Kamp H. Demonstrating the reliability of in vivo metabolomics based chemical grouping: towards best practice. Arch Toxicol 2024; 98:1111-1123. [PMID: 38368582 PMCID: PMC10944399 DOI: 10.1007/s00204-024-03680-y] [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: 11/30/2023] [Accepted: 01/15/2024] [Indexed: 02/19/2024]
Abstract
While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography-mass spectrometry (LC-MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.
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Affiliation(s)
- Mark R Viant
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - E Amstalden
- Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - T Athersuch
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - M Bouhifd
- European Chemicals Agency, Telakkakatu 6, FI-00121, Helsinki, Finland
| | - S Camuzeaux
- Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, W12 0NN, UK
| | - D M Crizer
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - P Driemert
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - T Ebbels
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - D Ekman
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - B Flick
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
- NUVISAN ICB GmbH, Toxicology, 13353, Berlin, Germany
| | - V Giri
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
| | - M Gómez-Romero
- Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, W12 0NN, UK
| | - V Haake
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - M Herold
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - A Kende
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - F Lai
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - P E G Leonards
- Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - P P Lim
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - G R Lloyd
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - J Mosley
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - C Namini
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - J R Rice
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - S Romano
- Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, 30605, USA
| | - C Sands
- Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, W12 0NN, UK
| | - M J Smith
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - T Sobanski
- European Chemicals Agency, Telakkakatu 6, FI-00121, Helsinki, Finland
| | - A D Southam
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - L Swindale
- Syngenta, Jealott's Hill International Research Centre, Bracknell, RG42 6EY, UK
| | - B van Ravenzwaay
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
- Environmental Sciences Consulting, 67122, Altrip, Germany
| | - T Walk
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
| | - R J M Weber
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - F M Zickgraf
- BASF SE, Carl-Bosch-Str 38, 67056, Ludwigshafen, Germany
| | - H Kamp
- BASF Metabolome Solutions GmbH, Tegeler Weg 33, 10589, Berlin, Germany
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Zhou Y, Chen Z, Yang M, Chen F, Yin J, Zhang Y, Zhou X, Sun X, Ni Z, Chen L, Lv Q, Zhu F, Liu S. FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response. Brief Bioinform 2024; 25:bbae223. [PMID: 38742521 PMCID: PMC11091744 DOI: 10.1093/bib/bbae223] [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: 10/17/2023] [Revised: 03/25/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.
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Affiliation(s)
- Yuan Zhou
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Mengjie Yang
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Fengyun Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Jiayi Yin
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xuheng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ziheng Ni
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lu Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Qun Lv
- Department of Respiratory, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 311121, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Shuiping Liu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
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8
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Tang J, Mou M, Zheng X, Yan J, Pan Z, Zhang J, Li B, Yang Q, Wang Y, Zhang Y, Gao J, Li S, Yang H, Zhu F. Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma. Anal Chem 2024; 96:4745-4755. [PMID: 38417094 DOI: 10.1021/acs.analchem.3c03796] [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: 03/01/2024]
Abstract
Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xin Zheng
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jin Yan
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Bo Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Song Li
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Hui Yang
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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9
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Yang Q, Chen S, Jiang W, Mi L, Liu J, Hu Y, Ji X, Wang J, Zhu F. MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics. Anal Chem 2024; 96:1410-1418. [PMID: 38221713 DOI: 10.1021/acs.analchem.3c03212] [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: 01/16/2024]
Abstract
Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.
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Affiliation(s)
- Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Shuman Chen
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Wenyu Jiang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Lan Mi
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jiarui Liu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yu Hu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Xinglai Ji
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jun Wang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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10
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Yin J, Chen Z, You N, Li F, Zhang H, Xue J, Ma H, Zhao Q, Yu L, Zeng S, Zhu F. VARIDT 3.0: the phenotypic and regulatory variability of drug transporter. Nucleic Acids Res 2024; 52:D1490-D1502. [PMID: 37819041 PMCID: PMC10767864 DOI: 10.1093/nar/gkad818] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/01/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.
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Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Nanxin You
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- The Children's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310052, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Jia Xue
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Hui Ma
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Qingwei Zhao
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Lushan Yu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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11
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Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, Wang S, Qiu Y, Chen Y, Zhu F. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Res 2024; 52:D1465-D1477. [PMID: 37713619 PMCID: PMC10767903 DOI: 10.1093/nar/gkad751] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/31/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.
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Affiliation(s)
- Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Donghai Zhao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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12
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Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Res 2024; 52:D1450-D1464. [PMID: 37850638 PMCID: PMC10767989 DOI: 10.1093/nar/gkad862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.
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Affiliation(s)
- Yintao Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yanfeng Hong
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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13
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Gong Y, Ding W, Wang P, Wu Q, Yao X, Yang Q. Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics. J Chem Inf Model 2023; 63:7628-7641. [PMID: 38079572 DOI: 10.1021/acs.jcim.3c01525] [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] [Indexed: 12/26/2023]
Abstract
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
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Affiliation(s)
- Yaguo Gong
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Wei Ding
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Qibiao Wu
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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14
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Jia W, Guo A, Bian W, Zhang R, Wang X, Shi L. Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways. Crit Rev Food Sci Nutr 2023:1-15. [PMID: 38127336 DOI: 10.1080/10408398.2023.2295016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.
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Affiliation(s)
- Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Agricultural Product Processing and Inspection Center, Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi, China
- Agricultural Product Quality Research Center, Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an, China
- Food Safety Testing Center, Shaanxi Sky Pet Biotechnology Co., Ltd, Xi'an, China
| | - Aiai Guo
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
| | - Wenwen Bian
- Agricultural Product Processing and Inspection Center, Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi, China
| | - Rong Zhang
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
| | - Xin Wang
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
| | - Lin Shi
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
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15
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Sun Z, Feng D, Jiang L, Tian J, Wang J, Zhu W. Integrated proteomic and metabolomic analysis of plasma reveals regulatory pathways and key elements in thyroid cancer. Mol Omics 2023; 19:800-809. [PMID: 37642188 DOI: 10.1039/d3mo00142c] [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: 08/31/2023]
Abstract
Thyroid cancer (TC) is the most common endocrine malignancy with increasing incidence in recent years. Fine-needle aspiration biopsy (FNAB), as a gold standard for the initial evaluation of thyroid nodules, fails to cover all the cytopathologic conditions resulting in overdiagnosis. There is an urgent need for a better classification of thyroid cancer from benign thyroid nodules (BTNs). Here, data independent acquisition (DIA)-based proteomics and untargeted metabolomics in plasma samples of 10 patients with TC and 15 patients with BTNs were performed. Key proteins and metabolites were identified specific to TC, and an independent cohort was used to validate the potential biomarkers using enzyme-linked immunosorbent assay (ELISA). In total, 1429 proteins and 1172 metabolites were identified. Principal component analysis showed a strong overlap at the proteomic level and a significant discrimination at the metabolomic level between the two groups, indicating a more drastic disturbance in the metabolome of thyroid cancer. Integrated analysis of proteomics and metabolomics shows glycerophospholipid metabolism and arachidonic acid metabolism as key regulatory pathways. Furthermore, a multi-omics biomarker panel was developed consisting of LCAT, GPX3 and leukotriene B4. Based on the AUC value for the discovery set, the classification performance was 0.960. The AUC value of the external validation set was 0.930. Altogether, our results will contribute to the clinical application of potential biomarkers in the diagnosis of thyroid cancer.
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Affiliation(s)
- Zijian Sun
- Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
| | - Dongdong Feng
- Department of Head and Neck Surgery, Center of Otolaryngology-head and neck surgery, Zhejiang Provincial People's Hospital (People's Hospital of Hangzhou Medical College), Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, 310014, China.
| | - Liehao Jiang
- Department of Head and Neck Surgery, Center of Otolaryngology-head and neck surgery, Zhejiang Provincial People's Hospital (People's Hospital of Hangzhou Medical College), Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, 310014, China.
| | - Jingkui Tian
- Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
| | - Jiafeng Wang
- Department of Head and Neck Surgery, Center of Otolaryngology-head and neck surgery, Zhejiang Provincial People's Hospital (People's Hospital of Hangzhou Medical College), Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, 310014, China.
| | - Wei Zhu
- Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
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16
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Wevers D, Ramautar R, Clark C, Hankemeier T, Ali A. Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics. Electrophoresis 2023; 44:2000-2024. [PMID: 37667867 DOI: 10.1002/elps.202300105] [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: 05/11/2023] [Revised: 08/08/2023] [Accepted: 08/19/2023] [Indexed: 09/06/2023]
Abstract
Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.
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Affiliation(s)
- Dirk Wevers
- Wageningen University and Research, Wageningen, The Netherlands
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Rawi Ramautar
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Charlie Clark
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Thomas Hankemeier
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Ahmed Ali
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
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17
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Manochkumar J, Cherukuri AK, Kumar RS, Almansour AI, Ramamoorthy S, Efferth T. A critical review of machine-learning for "multi-omics" marine metabolite datasets. Comput Biol Med 2023; 165:107425. [PMID: 37696182 DOI: 10.1016/j.compbiomed.2023.107425] [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: 05/30/2023] [Revised: 07/12/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.
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Affiliation(s)
- Janani Manochkumar
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Raju Suresh Kumar
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Abdulrahman I Almansour
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India.
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany.
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18
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Liang S, Zhao Y, Jin J, Qiao J, Wang D, Wang Y, Wei L. Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications. Comput Biol Med 2023; 164:107238. [PMID: 37515874 DOI: 10.1016/j.compbiomed.2023.107238] [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: 05/24/2023] [Revised: 06/16/2023] [Accepted: 07/07/2023] [Indexed: 07/31/2023]
Abstract
Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.
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Affiliation(s)
- Sirui Liang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yanxi Zhao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Junru Jin
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Jianbo Qiao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Ding Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yu Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China.
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19
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Alkady W, ElBahnasy K, Gad W. A diagnostic model for COVID-19 based on proteomics analysis. Comput Biol Med 2023; 162:107109. [PMID: 37276752 PMCID: PMC10232940 DOI: 10.1016/j.compbiomed.2023.107109] [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: 08/31/2022] [Revised: 05/21/2023] [Accepted: 05/30/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease. METHODS The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify the severity level of the COVID-19 infection. The proposed model is evaluated using four performance measures: accuracy, sensitivity, specificity, and precision. RESULTS The experiment results show that the proposed model accuracy can reach 80% using RF classifier with PCA. The PCA selects 22 proteins and 10 metabolites. While ANOVA selects 9 proteins and 5 metabolites. The accuracy reaches 92% after applying RF classifier with the ANOVA. Finally, the accuracy reaches 93% using the RF classifier with only ten features. The selected features are 7 proteins and 3 metabolites. Moreover, it shows that the selected features have a relation to the immune system and respiratory systems. CONCLUSION The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.
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Affiliation(s)
- Walaa Alkady
- Bioinformatics Program, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
| | - Khaled ElBahnasy
- Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
| | - Walaa Gad
- Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
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20
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Clark LE, Dickinson AJG, Lima S. GBA Regulates EMT/MET and Chemoresistance in Squamous Cell Carcinoma Cells by Modulating the Cellular Glycosphingolipid Profile. Cells 2023; 12:1886. [PMID: 37508550 PMCID: PMC10378370 DOI: 10.3390/cells12141886] [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: 06/02/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Glycosphingolipids (GSL) are plasma membrane components that influence molecular processes involved in cancer initiation, progression, and therapeutic responses. They also modulate receptor tyrosine kinases involved in EMT. Therefore, understanding the mechanisms that regulate GSLs in cancer has important therapeutic potential. One critical regulator of GSLs is the lysosomal glucosylceramidase β1 (GBA) that catalyzes the last step in GSL degradation. We show that, in cancer, GBA copy number amplifications and increased expression are widespread. We show that depleting GBA in squamous cell carcinoma cell lines results in a mesenchymal-to-epithelial shift, decreased invasion and migration, increased chemotherapeutic sensitivity, and decreased activation of receptor tyrosine kinases that are involved in regulating EMT. Untargeted lipidomics shows that GBA depletion had significant effects on sphingolipids and GSLs, suggesting that increased GBA activity in cancer sustains EMT and chemoresistance by modulating receptor tyrosine kinase activity and signaling via effects on the cellular lipid profile.
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Affiliation(s)
- Laura E Clark
- Department of Biology, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Amanda J G Dickinson
- Department of Biology, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Santiago Lima
- Department of Biology, Virginia Commonwealth University, Richmond, VA 23284, USA
- Massey Cancer Center, Richmond, VA 23298, USA
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21
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Vahabzadeh V, Moattar MH. Robust microarray data feature selection using a correntropy based distance metric learning approach. Comput Biol Med 2023; 161:107056. [PMID: 37235945 DOI: 10.1016/j.compbiomed.2023.107056] [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: 12/09/2022] [Revised: 04/18/2023] [Accepted: 05/20/2023] [Indexed: 05/28/2023]
Abstract
Classification of high-dimensional microarray data is a challenge in bioinformatics and genetic data processing. One of the challenging issues of feature selection is the presence of outliers. The Euclidean distance metric is sensitive to outliers. In this study, a distance metric learning based feature selection approach that uses the correntropy function as the discrimination metric is proposed. For this purpose, the metric learning problem is formulated as an optimization problem and solved using the Lagrange method. The output of the approach signifies the most important and robust features. After feature selection, different classification methods such as SVM, decision trees, and NN classifiers are used to investigate the classification accuracy of the proposed method as well as precision, recall, and F-measure. Experiments are carried out on 13 high-dimensional datasets and show that the proposed method outperforms the previous models in terms of accuracy and robustness.
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Affiliation(s)
- Venus Vahabzadeh
- Department of Software Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
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22
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Sadaqat M, Qasim M, Tahir Ul Qamar M, Masoud MS, Ashfaq UA, Noor F, Fatima K, Allemailem KS, Alrumaihi F, Almatroudi A. Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis. Comput Biol Med 2023; 161:107059. [PMID: 37244150 DOI: 10.1016/j.compbiomed.2023.107059] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/05/2023] [Accepted: 05/20/2023] [Indexed: 05/29/2023]
Abstract
Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun proto-oncogene (JUN), heat shot protein 90 AA1 (HSP90AA1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), and SRC proto-oncogene (SRC). Through, microarray data analysis, the expression level of JUN and IL6 were found to be upregulated while the expression level of HSP90AA1 was found to be downregulated. Kaplan-Meier survival analysis indicated that HSP90AA1 and JUN are promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver cancer. Moreover, the molecular docking and molecular dynamic simulation of 60ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between the compound and binding pockets of HSP90AA1 and JUN. Despite that, in vivo and in vitro studies are mandatory to unveil pharmacokinetics and biosafety profiles to completely track the candidature status of B. monnieri in liver cancer.
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Affiliation(s)
- Muhammad Sadaqat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Muhammad Qasim
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Muhammad Tahir Ul Qamar
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Muhammad Shareef Masoud
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Fatima Noor
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Kinza Fatima
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Khaled S Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
| | - Faris Alrumaihi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
| | - Ahmad Almatroudi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
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23
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Li S, Chen X, Chen J, Wu B, Liu J, Guo Y, Li M, Pu X. Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes. Comput Biol Med 2023; 161:106988. [PMID: 37201441 DOI: 10.1016/j.compbiomed.2023.106988] [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: 03/14/2023] [Revised: 03/30/2023] [Accepted: 04/27/2023] [Indexed: 05/20/2023]
Abstract
G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.
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Affiliation(s)
- Shiqi Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Xin Chen
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Jianfang Chen
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Binjian Wu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Jing Liu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
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24
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Walakira A, Skubic C, Nadižar N, Rozman D, Režen T, Mraz M, Moškon M. Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma. Comput Biol Med 2023; 159:106957. [PMID: 37116239 DOI: 10.1016/j.compbiomed.2023.106957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/17/2023] [Accepted: 04/16/2023] [Indexed: 04/30/2023]
Abstract
Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.
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Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | - Cene Skubic
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Nejc Nadižar
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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25
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Nie X, Qin D, Zhou X, Duo H, Hao Y, Li B, Liang G. Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges. Comput Biol Med 2023; 159:106939. [PMID: 37075602 DOI: 10.1016/j.compbiomed.2023.106939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.
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Affiliation(s)
- Xiner Nie
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China; College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Dan Qin
- Department of Biology, College of Science, Northeastern University, Boston, MA, 02115, USA
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China.
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26
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Fajarda O, Almeida JR, Duarte-Pereira S, Silva RM, Oliveira JL. Methodology to identify a gene expression signature by merging microarray datasets. Comput Biol Med 2023; 159:106867. [PMID: 37060770 DOI: 10.1016/j.compbiomed.2023.106867] [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: 10/19/2022] [Revised: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 04/17/2023]
Abstract
A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.
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Affiliation(s)
- Olga Fajarda
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal.
| | - João Rafael Almeida
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Sara Duarte-Pereira
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, Portugal.
| | - Raquel M Silva
- Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
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Wang Y, Liu X, Dong L, Cheng KK, Lin C, Wang X, Dong J, Deng L, Raftery D. iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions. Anal Chem 2023; 95:6203-6211. [PMID: 37023366 DOI: 10.1021/acs.analchem.2c04603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.
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Affiliation(s)
- Yongpei Wang
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Xingxing Liu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Liheng Dong
- School of Computing and Data Science, Xiamen University Malaysia, Sepang 43600, Malaysia
| | - Kian-Kai Cheng
- Department of Bioprocess and Polymer Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia
| | - Caigui Lin
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Xiaomin Wang
- Department of Hepatobiliary Surgery, Fujian Provincial Key Laboratory of Chronic Liver Disease and Hepatocellular Carcinoma, ZhongShan Hospital of Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, Washington 98109, United States
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Yang Q, Gong Y, Zhu F. Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics. Anal Chem 2023; 95:5542-5552. [PMID: 36944135 DOI: 10.1021/acs.analchem.2c04402] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.
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Affiliation(s)
- Qingxia Yang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yaguo Gong
- School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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Fu J, Zhu F, Xu CJ, Li Y. Metabolomics meets systems immunology. EMBO Rep 2023; 24:e55747. [PMID: 36916532 PMCID: PMC10074123 DOI: 10.15252/embr.202255747] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/24/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.
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Affiliation(s)
- Jianbo Fu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
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Mortezapour M, Tapak L, Bahreini F, Najafi R, Afshar S. Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis. Comput Biol Med 2023; 157:106779. [PMID: 36931200 DOI: 10.1016/j.compbiomed.2023.106779] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/10/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease. METHODS We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes. RESULTS In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2. CONCLUSION Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.
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Affiliation(s)
- Mahdie Mortezapour
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leili Tapak
- Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Fatemeh Bahreini
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Rezvan Najafi
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Saeid Afshar
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
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Nalepa J, Kotowski K, Machura B, Adamski S, Bozek O, Eksner B, Kokoszka B, Pekala T, Radom M, Strzelczak M, Zarudzki L, Krason A, Arcadu F, Tessier J. Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients. Comput Biol Med 2023; 154:106603. [PMID: 36738710 DOI: 10.1016/j.compbiomed.2023.106603] [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: 10/19/2022] [Revised: 01/11/2023] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.
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Affiliation(s)
- Jakub Nalepa
- Graylight Imaging, Gliwice, Poland; Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
| | | | | | | | - Oskar Bozek
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
| | - Bartosz Eksner
- Department of Radiology and Nuclear Medicine, ZSM Chorzów, Chorzów, Poland
| | - Bartosz Kokoszka
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Tomasz Pekala
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Mateusz Radom
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Marek Strzelczak
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Lukasz Zarudzki
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Agata Krason
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| | - Filippo Arcadu
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Informatics, Roche Innovation Center Basel, Basel, Switzerland
| | - Jean Tessier
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
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Baran Y, Doğan B. scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies. Comput Biol Med 2023; 155:106634. [PMID: 36774895 DOI: 10.1016/j.compbiomed.2023.106634] [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: 11/05/2022] [Revised: 01/28/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023]
Abstract
Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.
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Affiliation(s)
- Yusuf Baran
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey
| | - Berat Doğan
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey.
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Segura-Ortiz A, García-Nieto J, Aldana-Montes JF, Navas-Delgado I. GENECI: A novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks. Comput Biol Med 2023; 155:106653. [PMID: 36803795 DOI: 10.1016/j.compbiomed.2023.106653] [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: 11/16/2022] [Revised: 01/09/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.
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Affiliation(s)
- Adrián Segura-Ortiz
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain
| | - José García-Nieto
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain.
| | - José F Aldana-Montes
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - Ismael Navas-Delgado
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
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Marjit S, Bhattacharyya T, Chatterjee B, Sarkar R. Simulated annealing aided genetic algorithm for gene selection from microarray data. Comput Biol Med 2023; 158:106854. [PMID: 37023541 DOI: 10.1016/j.compbiomed.2023.106854] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/26/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic Algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic Algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://github.com/shyammarjit/SAGA.
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Wahid M, Saqib F, Akhtar S, Ali A, Tallei TE, Simal-Gandara J. Mechanistic insights of Cucumis melo L. seeds for gastrointestinal muscle spasms through calcium signaling pathway-related gene regulation networks in WGCNA and in vitro, in vivo studies. Comput Biol Med 2023; 155:106596. [PMID: 36773554 DOI: 10.1016/j.compbiomed.2023.106596] [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: 11/28/2022] [Revised: 12/26/2022] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND In addition to the nutritional benefits of Cucumis melo L., herbalists in Pakistan and India employ seeds to treat various ailments. This study aimed to determine the regulatory role of C. melo seeds in calcium-mediated smooth muscle contraction. METHODS We identified and quantified the phytochemicals of C. melo with LC ESI-MS/MS and HPLC, then conducted in vitro and in vivo tests to confirm the involvement in smooth muscle relaxation. Then, diarrhea-predominant irritable bowel syndrome gene datasets from NCBI GEO were acquired, DEGs and WGCNA followed by functional enrichment analysis. Next, molecular docking of key genes was performed. RESULTS The quantification of C. melo seeds revealed concentrations of rutin, kaempferol, and quercetin were 702.38 μg/g, 686.29 μg/g, and 658.41 μg/g, respectively. In vitro experiments revealed that C. melo seeds had a dose-dependent relaxant effect for potassium chloride (80 mM)-induced spastic contraction and exhibited calcium antagonistic response in calcium dose-response curves. In in vivo studies, Cm.EtOH exhibited antidiarrheal, antiperistaltic, and antisecretory effects. The functional enrichment of WGCNA and DEGs IBS-associated pathogenic genes, including those involved in calcium-mediated signaling, MAPK cascade, and inflammatory responses. MAPK1 and PIK3CG were identified as key genes with greater binding affinity with rutin, quercitrin, and kaempferol in molecular docking. CONCLUSIONS The bronchodilator and antidiarrheal effects of C. melo were produced by altering the regulatory genes of calcium-mediated smooth contraction.
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Affiliation(s)
- Muqeet Wahid
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60000, Pakistan.
| | - Fatima Saqib
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60000, Pakistan.
| | - Saeed Akhtar
- Institute of Food Science and Nutrition, Bahauddin Zakariya University, Multan, 60000, Pakistan.
| | - Anam Ali
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60000, Pakistan.
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, 95115, North Sulawesi, Indonesia.
| | - Jesus Simal-Gandara
- Universidade de Vigo, Nutrition and Bromatology Group, Analytical Chemistry and Food Science Department, Faculty of Science, E32004, Ourense, Spain.
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Zafari N, Bathaei P, Velayati M, Khojasteh-Leylakoohi F, Khazaei M, Fiuji H, Nassiri M, Hassanian SM, Ferns GA, Nazari E, Avan A. Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer. Comput Biol Med 2023; 155:106639. [PMID: 36805214 DOI: 10.1016/j.compbiomed.2023.106639] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/14/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023]
Abstract
The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.
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Affiliation(s)
- Nima Zafari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Parsa Bathaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahla Velayati
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Khojasteh-Leylakoohi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Fiuji
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammadreza Nassiri
- Recombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex, BN1 9PH, UK
| | - Elham Nazari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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Yan TC, Yue ZX, Xu HQ, Liu YH, Hong YF, Chen GX, Tao L, Xie T. A systematic review of state-of-the-art strategies for machine learning-based protein function prediction. Comput Biol Med 2023; 154:106446. [PMID: 36680931 DOI: 10.1016/j.compbiomed.2022.106446] [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: 11/22/2022] [Revised: 12/07/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.
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Affiliation(s)
- Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
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Wang T, Sun J, Zhao Q. Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism. Comput Biol Med 2023; 153:106464. [PMID: 36584603 DOI: 10.1016/j.compbiomed.2022.106464] [Citation(s) in RCA: 101] [Impact Index Per Article: 101.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Failure or inhibition of hERG channel activity caused by drug molecules can lead to prolonging QT interval, which will result in serious cardiotoxicity. Thus, evaluating the hERG blocking activity of all these small molecular compounds is technically challenging, and the relevant procedures are expensive and time-consuming. In this study, we develop a novel deep learning predictive model named DMFGAM for predicting hERG blockers. In order to characterize the molecule more comprehensively, we first consider the fusion of multiple molecular fingerprint features to characterize its final molecular fingerprint features. Then, we use the multi-head attention mechanism to extract the molecular graph features. Both molecular fingerprint features and molecular graph features are fused as the final features of the compounds to make the feature expression of compounds more comprehensive. Finally, the molecules are classified into hERG blockers or hERG non-blockers through the fully connected neural network. We conduct 5-fold cross-validation experiment to evaluate the performance of DMFGAM, and verify the robustness of DMFGAM on external validation datasets. We believe DMFGAM can serve as a powerful tool to predict hERG channel blockers in the early stages of drug discovery and development.
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Affiliation(s)
- Tianyi Wang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianqiang Sun
- School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
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Qiu S, Guo S, Yang Q, Xie Y, Tang S, Zhang A. Innovation in identifying metabolites from complex metabolome-Highlights of recent analytical platforms and protocols. Front Chem 2023; 11:1129717. [PMID: 36762198 PMCID: PMC9905695 DOI: 10.3389/fchem.2023.1129717] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 01/16/2023] [Indexed: 01/27/2023] Open
Abstract
Metabolites are closely intertwined genotypes that can provide clear information about the final phenotype. The high-throughput analysis platform used to identify candidate metabolites and describe their contributions can help to quickly detect metabolic characteristics from large spectral data, which may lead to peak data preprocessing, statistical analysis and functional interpretation. Developing a comprehensive strategy for discovering and verifying bioactive metabolites can provide a large number of new functional biomarkers, and then more closely reveal their functional changes, which has relevant biological significance for disease diagnosis and prognosis treatment.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Medical University, Haikou, China
| | - Sifan Guo
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Qiang Yang
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Medical University, Haikou, China,*Correspondence: Yiqiang Xie, ; Songqi Tang, ; Aihua Zhang,
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Medical University, Haikou, China,*Correspondence: Yiqiang Xie, ; Songqi Tang, ; Aihua Zhang,
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Medical University, Haikou, China,Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China,*Correspondence: Yiqiang Xie, ; Songqi Tang, ; Aihua Zhang,
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Fu J, Yang Q, Luo Y, Zhang S, Tang J, Zhang Y, Zhang H, Xu H, Zhu F. Label-free proteome quantification and evaluation. Brief Bioinform 2023; 24:6833644. [PMID: 36403090 DOI: 10.1093/bib/bbac477] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/24/2022] [Accepted: 10/08/2022] [Indexed: 11/21/2022] Open
Abstract
The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hanxiang Xu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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A new era begins at Nature Protocols. Nat Protoc 2023; 18:1. [PMID: 36564509 DOI: 10.1038/s41596-022-00798-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Xiang J, Wang X, Wang X, Zhang J, Yang S, Yang W, Han X, Liu Y. Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images. Comput Biol Med 2023; 152:106340. [PMID: 36481762 DOI: 10.1016/j.compbiomed.2022.106340] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 11/02/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND The workflow of prostate cancer diagnosis and grading is cumbersome and the results suffer from substantial inter-observer variability. Recent trials have shown potential in using machine learning to develop automated systems to address this challenge. Most automated deep learning systems for prostate cancer Gleason grading focused on supervised learning requiring demanding fine-grained pixel-level annotations. METHODS A weakly-supervised deep learning model with slide-level labels is presented in this study for the diagnosis and grading of prostate cancer with whole slide image (WSI). WSIs are first cropped into small patches and then processed with a deep learning model to extract patch-level features. A graph convolution network (GCN) is used to aggregate the features for classifications. Throughout the training process, the noisy labels are progressively filtered out to reduce inter-observer variations in clinical reports. Finally, multi-center independent test cohorts with 6,174 slides are collected to evaluate the prostate cancer diagnosis and grading performance of our model. RESULTS The cancer diagnosis (2-level classification) results on two external test sets (n= 4,675, n= 844) show an area under the receiver operating characteristic curve (AUC) of 0.985 and 0.986. The Gleason grading (6-level classification) results reach 0.931 quadratic weighted kappa on the internal test set (n= 531). It generalizes well on the external test dataset (n= 844) with 0.801 quadratic weighted kappa with the reference standard set independently. The model enables pathological meaningful interpretability by visualizing the most attended lesions which are highly consistent with expert annotations. CONCLUSION The proposed model incorporates a graph network in weakly supervised learning with only slide-level reports. A robust learning strategy is also employed to correct the label noise. It is highly accurate (>0.985 AUC for diagnosis) and also interpretable with intuitive heatmap visualization. It can be unified with a digital pathology pipeline to deliver prostate cancer metrics for a pathology report.
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Affiliation(s)
| | - Xiyue Wang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Xinran Wang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | | | - Sen Yang
- AI Lab, Tencent, Shenzhen, China
| | - Wei Yang
- AI Lab, Tencent, Shenzhen, China
| | - Xiao Han
- AI Lab, Tencent, Shenzhen, China
| | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
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Wang X, Zhang X, Li J, Fu J, Zhao M, Zhang W, Weng W, Li Q. Network pharmacology and LC-MS approachs to explore the active compounds and mechanisms of Yuanjiang decoction for treating bradyarrhythmia. Comput Biol Med 2023; 152:106435. [PMID: 36535207 DOI: 10.1016/j.compbiomed.2022.106435] [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: 08/24/2022] [Revised: 11/20/2022] [Accepted: 12/13/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Yuanjiang decoction (YJD), a traditional Chinese medicinal prescription, has been found to have a significant heart rate-increasing effect and is effective in the treatment of symptomatic bradyarrhythmia in previous studies. However, its specific components and potential mechanisms remain unclear. METHODS In this study, we detected and identified the main compounds of YJD using liquid chromatography-mass spectrometry (LC-MS). Through the approach of network pharmacology, we predicted the core targets of the active components, bradyarrhythmia targets, and obtained potential anti-bradyarrhythmia targets of YJD. We further performed protein to protein interaction (PPI), gene ontology (GO) enrichment analyses and kyoto encyclopedia of genes and genomes (KEGG) signaling pathway analyses for core targets, and constructed network of key active ingredients-core targets of YJD. Finally, molecular docking and molecular dynamics simulation were performed for key active ingredients and core targets. RESULTS The YJD contains a total of 35 main chemical components. The key active ingredients-core targets network contains 36 nodes and 90 edges, including 20 key active ingredients and 16 core targets. The core targets in the PPI network were TP53, TNF, HRAS, PPARG, IL1B, KCNH2, SCN5A, IDH1, LMNA, ACHE, F2, DRD2, CALM1, KCNQ1, TNNI3, IDH2 and TNNT2. KEGG pathway analysis showed that YJD treatment of bradyarrhythmia mainly involves neuroactive ligand-receptor interaction, adrenergic signaling in cardiomyocytes, cAMP signaling pathway, calcium signaling pathway, cholinergic synaptic and serotonergic synapse signaling pathway. The biological processes mainly include regulation of hormone levels, regulation of cardiac contraction, chemical synaptic transmission, circadian rhythm, positive regulation of heart rate, smooth muscle contraction, response to metal ion, oxidation-reduction process, neurotransmitter transport and import across plasma membrane. Molecular docking and molecular dynamics simulation results showed that hesperidin and tetrahydropalmatine had higher affinity with DRD2 and KCNQ1, respectively. CONCLUSION This study reveals the pharmacodynamic material basis of YJD and its potential multicomponent-multitarget-multipathway pharmacological effects, predicted its potential anti-bradyarrhythmia mechanism may be related to the regulation of myocardial autonomic nervous function and related ion channels. Our work demonstrates that YJD has great potential for treating bradyarrhythmias as a complementary medicine, and the results can provide a theoretical basis for the development and clinical application of YJD.
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Affiliation(s)
- Xujie Wang
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; China Academy of Chinese Medical Sciences, Beijing, China
| | - Xuexue Zhang
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; China Academy of Chinese Medical Sciences, Beijing, China
| | - Jiaxi Li
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; Shanxi University of Chinese Medicine, Taiyuan, China
| | - Jinyi Fu
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; China Academy of Chinese Medical Sciences, Beijing, China
| | - Mengjie Zhao
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Wantong Zhang
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; National Clinical Research Center for Chinese Medicine Cardiology, Beijing, China; NMPA Key Laboratory for Clinical Research and Evaluation of Traditional Chinese Medicine, Beijing, China.
| | - Weiliang Weng
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; National Clinical Research Center for Chinese Medicine Cardiology, Beijing, China; NMPA Key Laboratory for Clinical Research and Evaluation of Traditional Chinese Medicine, Beijing, China.
| | - Qiuyan Li
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; National Clinical Research Center for Chinese Medicine Cardiology, Beijing, China.
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Yue ZX, Yan TC, Xu HQ, Liu YH, Hong YF, Chen GX, Xie T, Tao L. A systematic review on the state-of-the-art strategies for protein representation. Comput Biol Med 2023; 152:106440. [PMID: 36543002 DOI: 10.1016/j.compbiomed.2022.106440] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.
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Affiliation(s)
- Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
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Liu C, Zhou Y, Zhou Y, Tang X, Tang L, Wang J. Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning. Comput Biol Med 2023; 152:106388. [PMID: 36470144 DOI: 10.1016/j.compbiomed.2022.106388] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/22/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS. METHODS Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores. RESULTS Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression. CONCLUSION Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.
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Affiliation(s)
- Chunjiang Liu
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China
| | - Yufei Zhou
- Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Yue Zhou
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China
| | - Xiaoqi Tang
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China
| | - Liming Tang
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China.
| | - Jiajia Wang
- Department of Rheumatology, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China.
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Basavarajappa GM, Rehman A, Shiroorkar PN, Sreeharsha N, Anwer MK, Aloufi B. Therapeutic effects of Crataegus monogyna inhibitors against breast cancer. Front Pharmacol 2023; 14:1187079. [PMID: 37180727 PMCID: PMC10174464 DOI: 10.3389/fphar.2023.1187079] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 04/10/2023] [Indexed: 05/16/2023] Open
Abstract
Breast cancer is a silent killer disorder among women and a serious economic burden in healthcare management. Every 19 s, a woman is diagnosed with breast cancer, and every 74 s, a woman worldwide passes away from the disease. Despite the increase in progressive research, advanced treatment approaches, and preventive measures, breast cancer rates continue to increase. This study provides a combination of data mining, network pharmacology, and docking analysis that surely could revolutionize cancer treatment by exploiting prestigious phytochemicals. Crataegus monogyna is a small, rounded deciduous tree with glossy, deeply lobed leaves and flat sprays of cream flowers, followed by dark red berries in autumn. Various studies demonstrated that C. monogyna is therapeutically effective against breast cancer. However, the particular molecular mechanism is still unknown. This study is credited for locating bioactive substances, metabolic pathways, and target genes for breast cancer treatment. According to the current investigation, which examined compound-target genes-pathway networks, it was found that the bioactive compounds of C. monogyna may operate as a viable solution against breast cancer by altering the target genes implicated in the disease pathogenesis. The expression level of target genes was analyzed using GSE36295 microarray data. Docking analysis and molecular dynamic simulation studies further strengthened the current findings by validating the effective activity of the bioactive compounds against putative target genes. In summary, we propose that six key compounds, luteolin, apigenin, quercetin, kaempferol, ursolic acid, and oleanolic acid, contributed to the development of breast cancer by affecting the MMP9 and PPARG proteins. Integration of network pharmacology and bioinformatics revealed C. monogyna's multitarget pharmacological mechanisms against breast cancer. This study provides convincing evidence that C. monogyna might partially alleviate breast cancer and ultimately lays a foundation for further experimental research on the anti-breast cancer activity of C. monogyna.
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Affiliation(s)
| | - Abdur Rehman
- College of Life Sciences, Northwest A&F University, Yangling, China
- *Correspondence: Nagaraja Sreeharsha, ; Abdur Rehman,
| | | | - Nagaraja Sreeharsha
- Department of Pharmaceutical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Hofuf, Saudi Arabia
- Department of Pharmaceutics, Vidya Siri College of Pharmacy, Bangalore, India
- *Correspondence: Nagaraja Sreeharsha, ; Abdur Rehman,
| | - Md. Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Alkharj, Saudi Arabia
| | - Bandar Aloufi
- Department of Biology, College of Science, University of Hail, Hail, Saudi Arabia
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Mou M, Pan Z, Lu M, Sun H, Wang Y, Luo Y, Zhu F. Application of Machine Learning in Spatial Proteomics. J Chem Inf Model 2022; 62:5875-5895. [PMID: 36378082 DOI: 10.1021/acs.jcim.2c01161] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.
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Affiliation(s)
- Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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Li Q, Wu J, Zhu M, Tang Y, Jin L, Chen Y, Jin M, Peng Z. A novel risk signature based on autophagy-related genes to evaluate tumor immune microenvironment and predict prognosis in hepatocellular carcinoma. Comput Biol Med 2022. [DOI: 10.1016/j.compbiomed.2022.106437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Tang H, Sun L, Huang J, Yang Z, Li C, Zhou X. The mechanism and biomarker function of Cavin-2 in lung ischemia-reperfusion injury. Comput Biol Med 2022; 151:106234. [PMID: 36335812 DOI: 10.1016/j.compbiomed.2022.106234] [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: 08/31/2022] [Revised: 10/01/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Lung Ischemia Reperfusion injury(LIRI) is one of the most predominant complications of ischemic lung disease. Cavin-2 emerged as a regulator of a variety of cellular processes, including endocytosis, lipid homeostasis, signal transduction and tumorigenesis, but the function of Cavin-2 in LIRI is unknown. The purpose of this study was to determine the predictive potential of Cavin-2 in protecting lung ischemia-reperfusion injury and its corresponding mechanisms. METHODS We found the strong relationship between Cavin-2 and multiple immune-related genes by deep learning method. To reveal the mechanism of Cavin-2 in LIRI, the LIRI SD rat model was constructed to detect the expression of Cavin-2 in the lung tissue of SD rats after LIRI, and the expression of Cavin-2 in lung cell lines was also detected. The expression of IL-6, IL-10 and MDA in cells after Cavin-2 over-expression or knockdown was examined under hypoxic conditions. The expression levels of p-AKT, p-STAT3 and p-ERK1/2 were measured in over-expressing Cavin-2 cells under hypoxic-ischemia conditions, and then the corresponding blockers of AKT, STAT3 and ERK1/2 were given to verify, whether they play a protective role in LIRI. RESULTS After hypoxia, the expression of Cavin-2 in rat lung tissues was significantly increased, and the cellular activity and IL-10 in Cavin-2 over-expressing cells were significantly higher than that of the control group, while IL-6 and MDA were significantly lower than that of the control group, while the above results were reversed in Cavin-2 knockdown cells; Meanwhile, the phosphorylation levels of AKT, STAT3, and ERK1/2 were significantly increased in Cavin-2 over-expression cells after hypoxia. When AKT, STAT3, and ERK1/2 specific blockers were given, they lost their protective effect against LIRI. CONCLUSIONS Cavin-2 shows biomarker potential in protecting lung from ischemia-reperfusion injury through the survivor activating factor enhancement (SAFE) and reperfusion injury salvage kinase (RISK) pathway.
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Affiliation(s)
- Hexiao Tang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Linao Sun
- Tianjin Medical University, Tianjin, China
| | - Jingyu Huang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zetian Yang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Changsheng Li
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
| | - Xuefeng Zhou
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
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