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Cheng X, Meng X, Chen R, Song Z, Li S, Wei S, Lv H, Zhang S, Tang H, Jiang Y, Zhang R. The molecular subtypes of autoimmune diseases. Comput Struct Biotechnol J 2024; 23:1348-1363. [PMID: 38596313 PMCID: PMC11001648 DOI: 10.1016/j.csbj.2024.03.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024] Open
Abstract
Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.
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Affiliation(s)
| | | | | | - Zerun Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuai Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuhao Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hao Tang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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2
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Priyanka P, Gopalakrishnan AP, Nisar M, Shivamurthy PB, George M, John L, Sanjeev D, Yandigeri T, Thomas SD, Rafi A, Dagamajalu S, Velikkakath AKG, Abhinand CS, Kanekar S, Prasad TSK, Balaya RDA, Raju R. A global phosphosite-correlated network map of Thousand And One Kinase 1 (TAOK1). Int J Biochem Cell Biol 2024; 170:106558. [PMID: 38479581 DOI: 10.1016/j.biocel.2024.106558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 02/19/2024] [Accepted: 03/09/2024] [Indexed: 03/25/2024]
Abstract
Thousand and one amino acid kinase 1 (TAOK1) is a sterile 20 family Serine/Threonine kinase linked to microtubule dynamics, checkpoint signaling, DNA damage response, and neurological functions. Molecular-level alterations of TAOK1 have been associated with neurodevelopment disorders and cancers. Despite their known involvement in physiological and pathophysiological processes, and as a core member of the hippo signaling pathway, the phosphoregulatory network of TAOK1 has not been visualized. Aimed to explore this network, we first analyzed the predominantly detected and differentially regulated TAOK1 phosphosites in global phosphoproteome datasets across diverse experimental conditions. Based on 709 qualitative and 210 quantitative differential cellular phosphoproteome datasets that were systematically assembled, we identified that phosphorylation at Ser421, Ser9, Ser965, and Ser445 predominantly represented TAOK1 in almost 75% of these datasets. Surprisingly, the functional role of all these phosphosites in TAOK1 remains unexplored. Hence, we employed a robust strategy to extract the phosphosites in proteins that significantly correlated in expression with predominant TAOK1 phosphosites. This led to the first categorization of the phosphosites including those in the currently known and predicted interactors, kinases, and substrates, that positively/negatively correlated with the expression status of each predominant TAOK1 phosphosites. Subsequently, we also analyzed the phosphosites in core proteins of the hippo signaling pathway. Based on the TAOK1 phosphoregulatory network analysis, we inferred the potential role of the predominant TAOK1 phosphosites. Especially, we propose pSer9 as an autophosphorylation and TAOK1 kinase activity-associated phosphosite and pS421, the most frequently detected phosphosite in TAOK1, as a significant regulatory phosphosite involved in the maintenance of genome integrity. Considering that the impact of all phosphosites that predominantly represent each kinase is essential for the efficient interpretation of global phosphoproteome datasets, we believe that the approach undertaken in this study is suitable to be extended to other kinases for accelerated research.
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Affiliation(s)
- Pahal Priyanka
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Athira Perunelly Gopalakrishnan
- Center for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Mahammad Nisar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | | | - Mejo George
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Diya Sanjeev
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Tanuja Yandigeri
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Sonet D Thomas
- Center for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Ahmad Rafi
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Shobha Dagamajalu
- Center for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Anoop Kumar G Velikkakath
- Center for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Chandran S Abhinand
- Center for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.
| | - Saptami Kanekar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
| | | | | | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, India.
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3
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Lai W, Xie R, Chen C, Lou W, Yang H, Deng L, Lu Q, Tang X. Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC. Heliyon 2024; 10:e28490. [PMID: 38590858 PMCID: PMC10999934 DOI: 10.1016/j.heliyon.2024.e28490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/10/2024] Open
Abstract
Background High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice. Methods We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments. Results We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment. Conclusions We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.
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Affiliation(s)
- Wenwen Lai
- Department of Organ Transplantation, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Ruixiang Xie
- School of Life Science, Nanchang University, Nanchang University, Nanchang, China
| | - Chen Chen
- College of Basic Medical Science, Nanchang University, Nanchang, China
| | - Weiming Lou
- Academic Affairs Office, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Haiyan Yang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Quqin Lu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Xiaoli Tang
- College of Basic Medical Science, Nanchang University, Nanchang, China
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Cao J, Chen Q, Qiu J, Wang Y, Lan W, Du X, Tan K. NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network. J Cell Mol Med 2024; 28:e18224. [PMID: 38509739 PMCID: PMC10955156 DOI: 10.1111/jcmm.18224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/22/2024] Open
Abstract
Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.
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Affiliation(s)
- Junyue Cao
- College of Life Science and TechnologyGuangxi UniversityNanningChina
| | - Qingfeng Chen
- School of Computer, Electronics and InformationGuangxi UniversityNanningChina
| | - Junlai Qiu
- School of Computer, Electronics and InformationGuangxi UniversityNanningChina
| | - Yiming Wang
- School of Computer, Electronics and InformationGuangxi UniversityNanningChina
| | - Wei Lan
- School of Computer, Electronics and InformationGuangxi UniversityNanningChina
| | - Xiaojing Du
- School of Computer, Electronics and InformationGuangxi UniversityNanningChina
| | - Kai Tan
- School of Computer, Electronics and InformationGuangxi UniversityNanningChina
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5
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Chen S, Li M, Semenov I. MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework. Methods 2024; 224:79-92. [PMID: 38430967 DOI: 10.1016/j.ymeth.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/16/2024] [Accepted: 02/23/2024] [Indexed: 03/05/2024] Open
Abstract
The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can leverage multiple sources of information, including sequence data that contains biological structural information, and interaction data. While useful, these methods cannot be effectively applied to each type of information individually (e.g., chemical structure and interaction network) and do not take into account the specificity of DTI data such as low- or zero-interaction biological entities. To overcome these limitations, we propose a method called MFA-DTI (Multi-feature Fusion Adopted framework for DTI). MFA-DTI consists of three modules: an interaction graph learning module that processes the interaction network to generate interaction vectors, a chemical structure learning module that extracts features from the chemical structure, and a fusion module that combines these features for the final prediction. To validate the performance of MFA-DTI, we conducted experiments on six public datasets under different settings. The results indicate that the proposed method is highly effective in various settings and outperforms state-of-the-art methods.
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Affiliation(s)
- Siqi Chen
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, China.
| | - Minghui Li
- Beidahuang Industry Group General Hospital, Harbin, 150006, China
| | - Ivan Semenov
- College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China
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6
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Idrees S, Paudel KR, Sadaf T, Hansbro PM. Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods. FEBS Lett 2024; 598:725-742. [PMID: 38439692 DOI: 10.1002/1873-3468.14841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/09/2024] [Accepted: 02/18/2024] [Indexed: 03/06/2024]
Abstract
Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Philip M Hansbro
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
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7
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Liu C, Xiao K, Yu C, Lei Y, Lyu K, Tian T, Zhao D, Zhou F, Tang H, Zeng J. A probabilistic knowledge graph for target identification. PLoS Comput Biol 2024; 20:e1011945. [PMID: 38578805 DOI: 10.1371/journal.pcbi.1011945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 04/22/2024] [Accepted: 02/24/2024] [Indexed: 04/07/2024] Open
Abstract
Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.
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Affiliation(s)
- Chang Liu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Kaimin Xiao
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
- Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing, China
| | - Cuinan Yu
- Machine Learning Department, Silexon AI Technology Co., Ltd., Nanjing, Jiangsu Province, China
| | - Yipin Lei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Kangbo Lyu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Tingzhong Tian
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, China
| | - Haidong Tang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Jianyang Zeng
- School of Engineering, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Research Center for Industries of the Future and School of Engineering, Westlake University, Hangzhou, Zhejiang Province, China
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8
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Kim KM, Lee KG, Lee S, Hong BK, Yun H, Park YJ, Yoo SA, Kim WU. The acute phase reactant orosomucoid-2 directly promotes rheumatoid inflammation. Exp Mol Med 2024:10.1038/s12276-024-01188-0. [PMID: 38556552 DOI: 10.1038/s12276-024-01188-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 12/04/2023] [Accepted: 12/20/2023] [Indexed: 04/02/2024] Open
Abstract
Acute phase proteins involved in chronic inflammatory diseases have not been systematically analyzed. Here, global proteome profiling of serum and urine revealed that orosomucoid-2 (ORM2), an acute phase reactant, was differentially expressed in rheumatoid arthritis (RA) patients and showed the highest fold change. Therefore, we questioned the extent to which ORM2, which is produced mainly in the liver, actively participates in rheumatoid inflammation. Surprisingly, ORM2 expression was upregulated in the synovial fluids and synovial membranes of RA patients. The major cell types producing ORM2 were synovial macrophages and fibroblast-like synoviocytes (FLSs) from RA patients. Recombinant ORM2 robustly increased IL-6, TNF-α, CXCL8 (IL-8), and CCL2 production by RA macrophages and FLSs via the NF-κB and p38 MAPK pathways. Interestingly, glycophorin C, a membrane protein for determining erythrocyte shape, was the receptor for ORM2. Intra-articular injection of ORM2 increased the severity of arthritis in mice and accelerated the infiltration of macrophages into the affected joints. Moreover, circulating ORM2 levels correlated with RA activity and radiographic progression. In conclusion, the acute phase protein ORM2 can directly increase the production of proinflammatory mediators and promote chronic arthritis in mice, suggesting that ORM2 could be a new therapeutic target for RA.
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Affiliation(s)
- Ki-Myo Kim
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Kang-Gu Lee
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Saseong Lee
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea
| | - Bong-Ki Hong
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea
| | - Heejae Yun
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Yune-Jung Park
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, The Catholic University of Korea, Suwon, South Korea
| | - Seung-Ah Yoo
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea.
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
| | - Wan-Uk Kim
- Center for Integrative Rheumatoid Transcriptomics and Dynamics, The Catholic University of Korea, Seoul, South Korea.
- Department of Internal Medicine, The Catholic University of Korea, Seoul, South Korea.
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E Z, Qiao G, Wang G, Li Y. GSL-DTI: Graph structure learning network for Drug-Target interaction prediction. Methods 2024; 223:136-145. [PMID: 38360082 DOI: 10.1016/j.ymeth.2024.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 12/23/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
MOTIVATION Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. RESULTS We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.
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Affiliation(s)
- Zixuan E
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China
| | - Guanyu Qiao
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China.
| | - Yang Li
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China.
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Jin S, Zhang Y, Yu H, Lu M. SADR: Self-Supervised Graph Learning With Adaptive Denoising for Drug Repositioning. IEEE/ACM Trans Comput Biol Bioinform 2024; 21:265-277. [PMID: 38190661 DOI: 10.1109/tcbb.2024.3351079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications.
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Liu Q, Li X, Li Y, Luo Q, Fan Q, Lu A, Guan D, Li J. A novel network pharmacology strategy to decode mechanism of Wuling Powder in treating liver cirrhosis. Chin Med 2024; 19:36. [PMID: 38429802 PMCID: PMC10905787 DOI: 10.1186/s13020-024-00896-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/26/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Liver cirrhosis is a chronic liver disease with hepatocyte necrosis and lesion. As one of the TCM formulas Wuling Powder (WLP) is widely used in the treatment of liver cirrhosis. However, it's key functional components and action mechanism still remain unclear. We attempted to explore the Key Group of Effective Components (KGEC) of WLP in the treatment of Liver cirrhosis through integrative pharmacology combined with experiments. METHODS The components and potential target genes of WLP were extracted from published databases. A novel node importance calculation model considering both node control force and node bridging force is designed to construct the Function Response Space (FRS) and obtain key effector proteins. The genetic knapsack algorithm was employed to select KGEC. The effectiveness and reliability of KGEC were evaluated at the functional level by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the effectiveness and potential mechanism of KGEC were confirmed by CCK-8, qPCR and Western blot. RESULTS 940 effective proteins were obtained in FRS. KEGG pathways and GO terms enrichments analysis suggested that effective proteins well reflect liver cirrhosis characteristics at the functional level. 29 components of WLP were defined as KGEC, which covered 100% of the targets of the effective proteins. Additionally, the pathways enriched for the KGEC targets accounted for 83.33% of the shared genes between the targets and the pathogenic genes enrichment pathways. Three components scopoletin, caryophyllene oxide, and hydroxyzinamic acid from KGEC were selected for in vivo verification. The qPCR results demonstrated that all three components significantly reduced the mRNA levels of COL1A1 in TGF-β1-induced liver cirrhosis model. Furthermore, the Western blot assay indicated that these components acted synergistically to target the NF-κB, AMPK/p38, cAMP, and PI3K/AKT pathways, thus inhibiting the progression of liver cirrhosis. CONCLUSION In summary, we have developed a new model that reveals the key components and potential mechanisms of WLP for the treatment of liver cirrhosis. This model provides a reference for the secondary development of WLP and offers a methodological strategy for studying TCM formulas.
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Affiliation(s)
- Qinwen Liu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Xiaowei Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Yi Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Qian Luo
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Qiling Fan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Aiping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China.
- Guangdong-Hong Kong-Macau Joint Lab On Chinese Medicine and Immune Disease Research, Guangzhou, China.
| | - Daogang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China.
| | - Jiahui Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
- Center for Genetics and Developmental Systems Biology, Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
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12
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Karunakaran KB, Jain S, Brahmachari SK, Balakrishnan N, Ganapathiraju MK. Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes. Schizophrenia (Heidelb) 2024; 10:26. [PMID: 38413605 PMCID: PMC10899210 DOI: 10.1038/s41537-024-00439-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/24/2024] [Indexed: 02/29/2024]
Abstract
Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.
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Affiliation(s)
- Kalyani B Karunakaran
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India.
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan.
| | - Sanjeev Jain
- National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, India.
| | | | - N Balakrishnan
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India
| | - Madhavi K Ganapathiraju
- Department of Computer Science, Carnegie Mellon University Qatar, Doha, Qatar.
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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13
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Xiong D, Qiu Y, Zhao J, Zhou Y, Lee D, Gupta S, Torres M, Lu W, Liang S, Kang JJ, Eng C, Loscalzo J, Cheng F, Yu H. Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations. bioRxiv 2024:2023.04.24.538110. [PMID: 37162909 PMCID: PMC10168245 DOI: 10.1101/2023.04.24.538110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.
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Affiliation(s)
- Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Junfei Zhao
- Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY 10032, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dongjin Lee
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Shobhita Gupta
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
- Biophysics Program, Cornell University, Ithaca, NY 14853, USA
| | - Mateo Torres
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Jin Joo Kang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
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14
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Dehghan A, Abbasi K, Razzaghi P, Banadkuki H, Gharaghani S. CCL-DTI: contributing the contrastive loss in drug-target interaction prediction. BMC Bioinformatics 2024; 25:48. [PMID: 38291364 DOI: 10.1186/s12859-024-05671-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module. RESULTS In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets. CONCLUSIONS Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.
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Affiliation(s)
- Alireza Dehghan
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, 1417614411, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics and Artificial Intelligence in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, 1417614411, Iran
| | - Parvin Razzaghi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 4513766731, Iran.
| | - Hossein Banadkuki
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 1417614411, Iran
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 1417614411, Iran.
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15
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Duman ET, Tuna G, Ak E, Avsar G, Pir P. Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19. Sci Rep 2024; 14:2325. [PMID: 38282038 PMCID: PMC10822845 DOI: 10.1038/s41598-024-52819-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 01/24/2024] [Indexed: 01/30/2024] Open
Abstract
A novel virus emerged from Wuhan, China, at the end of 2019 and quickly evolved into a pandemic, significantly impacting various industries, especially healthcare. One critical lesson from COVID-19 is the importance of understanding and predicting underlying comorbidities to better prioritize care and pharmacological therapies. Factors like age, race, and comorbidity history are crucial in determining disease mortality. While clinical data from hospitals and cohorts have led to the identification of these comorbidities, traditional approaches often lack a mechanistic understanding of the connections between them. In response, we utilized a deep learning approach to integrate COVID-19 data with data from other diseases, aiming to detect comorbidities with mechanistic insights. Our modified algorithm in the mpDisNet package, based on word-embedding deep learning techniques, incorporates miRNA expression profiles from SARS-CoV-2 infected cell lines and their target transcription factors. This approach is aligned with the emerging field of network medicine, which seeks to define diseases based on distinct pathomechanisms rather than just phenotypes. The main aim is discovery of possible unknown comorbidities by connecting the diseases by their miRNA mediated regulatory interactions. The algorithm can predict the majority of COVID-19's known comorbidities, as well as several diseases that have yet to be discovered to be comorbid with COVID-19. These potentially comorbid diseases should be investigated further to raise awareness and prevention, as well as informing the comorbidity research for the next possible outbreak.
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Affiliation(s)
- Emre Taylan Duman
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
- NGS-Core Unit for Integrative Genomics, Institute of Pathology, University Medical Center Göttingen, Göttingen, Germany.
| | - Gizem Tuna
- Department of Molecular Biology, Gebze Technical University, Kocaeli, Turkey
| | - Enes Ak
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Gülben Avsar
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Pinar Pir
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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16
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Kong X, Diao L, Jiang P, Nie S, Guo S, Li D. DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge. Brief Bioinform 2024; 25:bbae111. [PMID: 38517698 PMCID: PMC10959161 DOI: 10.1093/bib/bbae111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/24/2024] Open
Abstract
The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.
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Affiliation(s)
- Xiangren Kong
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lihong Diao
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Peng Jiang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shiyan Nie
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shuzhen Guo
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Dong Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
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17
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Hu C, Mi W, Li F, Zhu L, Ou Q, Li M, Li T, Ma Y, Zhang Y, Xu Y. Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer. Sci Data 2024; 11:74. [PMID: 38228620 PMCID: PMC10791624 DOI: 10.1038/s41597-024-02915-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 01/03/2024] [Indexed: 01/18/2024] Open
Abstract
Combination therapy can greatly improve the efficacy of cancer treatment, so identifying the most effective drug combination and interaction can accelerate the development of combination therapy. Here we developed a computational network biological approach to identify the effective drug which inhibition risk pathway crosstalk of cancer, and then filtrated and optimized the drug combination for cancer treatment. We integrated high-throughput data concerning pan-cancer and drugs to construct miRNA-mediated crosstalk networks among cancer pathways and further construct networks for therapeutic drug. Screening by drug combination method, we obtained 687 optimized drug combinations of 83 first-line anticancer drugs in pan-cancer. Next, we analyzed drug combination mechanism, and confirmed that the targets of cancer-specific crosstalk network in drug combination were closely related to cancer prognosis by survival analysis. Finally, we save all the results to a webpage for query ( http://bio-bigdata.hrbmu.edu.cn/oDrugCP/ ). In conclusion, our study provided an effective method for screening precise drug combinations for various cancer treatments, which may have important scientific significance and clinical application value for tumor treatment.
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Affiliation(s)
- Congxue Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Wanqi Mi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Lun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Qi Ou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Maohao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tengyue Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yuheng Ma
- Department of Pharmacy, Inner Mongolia Medical University, Jinshan Development Zone, Hohhot, 010100, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yingqi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
- Department of Pharmacy, Inner Mongolia Medical University, Jinshan Development Zone, Hohhot, 010100, China.
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18
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Elkin R, Oh JH, Dela Cruz F, Norton L, Deasy JO, Kung AL, Tannenbaum AR. Dynamic network curvature analysis of gene expression reveals novel potential therapeutic targets in sarcoma. Sci Rep 2024; 14:488. [PMID: 38177639 PMCID: PMC10766622 DOI: 10.1038/s41598-023-49930-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/13/2023] [Indexed: 01/06/2024] Open
Abstract
Network properties account for the complex relationship between genes, making it easier to identify complex patterns in their interactions. In this work, we leveraged these network properties for dual purposes. First, we clustered pediatric sarcoma tumors using network information flow as a similarity metric, computed by the Wasserstein distance. We demonstrate that this approach yields the best concordance with histological subtypes, validated against three state-of-the-art methods. Second, to identify molecular targets that would be missed by more conventional methods of analysis, we applied a novel unsupervised method to cluster gene interactomes represented as networks in pediatric sarcoma. RNA-Seq data were mapped to protein-level interactomes to construct weighted networks that were then subjected to a non-Euclidean, multi-scale geometric approach centered on a discrete notion of curvature. This provides a measure of the functional association among genes in the context of their connectivity. In confirmation of the validity of this method, hierarchical clustering revealed the characteristic EWSR1-FLI1 fusion in Ewing sarcoma. Furthermore, assessing the effects of in silico edge perturbations and simulated gene knockouts as quantified by changes in curvature, we found non-trivial gene associations not previously identified.
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Affiliation(s)
- Rena Elkin
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, 10065, USA.
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Filemon Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Larry Norton
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Andrew L Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Allen R Tannenbaum
- Departments of Computer Science and Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, USA
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Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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20
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Liu H, Xu Q, Wufuer H, Li Z, Sun R, Jiang Z, Dou X, Fu Q, Campisi J, Sun Y. Rutin is a potent senomorphic agent to target senescent cells and can improve chemotherapeutic efficacy. Aging Cell 2024; 23:e13921. [PMID: 37475161 PMCID: PMC10776113 DOI: 10.1111/acel.13921] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 05/24/2023] [Accepted: 06/20/2023] [Indexed: 07/22/2023] Open
Abstract
Aging is a major risk factor for most chronic disorders, for which cellular senescence is one of the central hallmarks. Senescent cells develop the pro-inflammatory senescence-associated secretory phenotype (SASP), which significantly contributes to organismal aging and age-related disorders. Development of senotherapeutics, an emerging class of therapeutic agents to target senescent cells, allows to effectively delay aging and alleviate chronic pathologies. Here we report preliminary outputs from screening of a natural medicinal agent (NMA) library for senotherapeutic candidates and validated several agents with prominent potential as senomorphics. Rutin, a phytochemical constituent found in a number of plants, showed remarkable capacity in targeting senescent cells by dampening expression of the full spectrum SASP. Further analysis indicated that rutin restrains the acute stress-associated phenotype (ASAP) by specifically interfering with the interactions of ATM with HIF1α, a master regulator of cellular and systemic homeostasis activated during senescence, and of ATM with TRAF6, part of a key signaling axis supporting the ASAP development toward the SASP. Conditioned media produced by senescent stromal cells enhanced the malignant phenotypes of prostate cancer cells, including in vitro proliferation, migration, invasion, and more importantly, chemoresistance, while rutin remarkably downregulated these gain-of-functions. Although classic chemotherapy reduced tumor progression, the treatment outcome was substantially improved upon combination of a chemotherapeutic agent with rutin. Our study provides a proof of concept for rutin as an emerging natural senomorphic agent, and presents an effective therapeutic avenue for alleviating age-related pathologies including cancer.
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Affiliation(s)
- Hanxin Liu
- Department of PharmacologyInstitute of Aging Medicine, Binzhou Medical UniversityYantaiChina
| | - Qixia Xu
- CAS Key Laboratory of Tissue Microenvironment and TumorShanghai Institute of Nutrition and Health, Chinese Academy of SciencesShanghaiChina
| | - Halidan Wufuer
- CAS Key Laboratory of Tissue Microenvironment and TumorShanghai Institute of Nutrition and Health, Chinese Academy of SciencesShanghaiChina
| | - Zi Li
- Shanghai Institute of Nutrition and Health, Chinese Academy of SciencesShanghaiChina
| | - Rong Sun
- Department of Discovery BiologyBioduro‐Sundia, Zhangjiang Hi‐Tech ParkShanghaiChina
| | - Zhirui Jiang
- CAS Key Laboratory of Tissue Microenvironment and TumorShanghai Institute of Nutrition and Health, Chinese Academy of SciencesShanghaiChina
| | - Xuefeng Dou
- CAS Key Laboratory of Tissue Microenvironment and TumorShanghai Institute of Nutrition and Health, Chinese Academy of SciencesShanghaiChina
| | - Qiang Fu
- Department of PharmacologyInstitute of Aging Medicine, Binzhou Medical UniversityYantaiChina
| | - Judith Campisi
- Buck Institute for Research on AgingNovatoCaliforniaUSA
- Lawrence Berkeley National LaboratoryUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Yu Sun
- Department of PharmacologyInstitute of Aging Medicine, Binzhou Medical UniversityYantaiChina
- CAS Key Laboratory of Tissue Microenvironment and TumorShanghai Institute of Nutrition and Health, Chinese Academy of SciencesShanghaiChina
- Department of Medicine and VAPSHCSUniversity of WashingtonSeattleWashingtonUSA
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21
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Csabai L, Bohár B, Türei D, Prabhu S, Földvári-Nagy L, Madgwick M, Fazekas D, Módos D, Ölbei M, Halka T, Poletti M, Kornilova P, Kadlecsik T, Demeter A, Szalay-Bekő M, Kapuy O, Lenti K, Vellai T, Gul L, Korcsmáros T. AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation. Autophagy 2024; 20:188-201. [PMID: 37589496 PMCID: PMC10761021 DOI: 10.1080/15548627.2023.2247737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/31/2023] [Accepted: 08/06/2023] [Indexed: 08/18/2023] Open
Abstract
Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.
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Affiliation(s)
- Luca Csabai
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Balázs Bohár
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | | | - László Földvári-Nagy
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Matthew Madgwick
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | - Dávid Fazekas
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dezső Módos
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | - Márton Ölbei
- Earlham Institute, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Themis Halka
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Martina Poletti
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | | | - Tamás Kadlecsik
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | | | | | - Orsolya Kapuy
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Katalin Lenti
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Tibor Vellai
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
- ELKH/MTA-ELTE Genetics Research Group, Budapest, Hungary
| | - Lejla Gul
- Earlham Institute, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Tamás Korcsmáros
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
- Quadram Institute, Norwich Research Park, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
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22
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Chen J, Li J, Zhong C, Ling Y, Liu D, Li X, Xu J, Liu Q, Guo Y, Wang L. Nanobody-loaded nanobubbles targeting the G250 antigen with ultrasound/photoacoustic/fluorescence multimodal imaging capabilities for specifically enhanced imaging of RCC. Nanoscale 2023; 16:343-359. [PMID: 38062769 DOI: 10.1039/d3nr04097f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Clinicians have attempted to discover a noninvasive, easy-to-perform, and accurate method to distinguish benign and malignant renal masses. The targeted nanobubbles (NBs) we constructed that target the specific membrane antigen of renal cell carcinoma (RCC), G250, and contain indocyanine green (ICG) provide multimodal enhanced imaging capability in ultrasound/photoacoustic/fluorescence for RCC which may possibly solve this problem. In this study, we encapsulated ICG in the lipid shell of the NBs by mechanical oscillation, then anti-G250 nanobodies (AGN) were coupled to the surfaces by the biotin-streptavidin bridge method, and the nanobubble named AGN/ICG-NB was completely constructed. The average particle diameter of the prepared AGN/ICG-NBs was (427.2 ± 4.50) nm, and the zeta potential was (-13.33 ± 1.01) mV. Immunofluorescence and flow cytometry confirmed the specific binding capability of AGN/ICG-NBs to G250-positive cells. In vitro imaging experiments confirmed the multimodal imaging capability of AGN/ICG-NBs, and the in vivo imaging experiments demonstrated the specifically enhanced ability of AGN/ICG-NBs for ultrasound/photoacoustic/fluorescence imaging of human-derived RCC tumors. The biosafety of AGN/ICG-NB was verified by CCK-8 assay, organ H&E staining and blood biochemical indices. In conclusion, the targeted nanobubbles we prepared with ultrasound/photoacoustic/fluorescence multimodal imaging capabilities provide a potentially feasible approach to address the need for early diagnosis and differential diagnosis of renal masses.
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Affiliation(s)
- Jiajiu Chen
- Department of Urology, Daping Hospital, Army Medical University, Chongqing 400042, P.R. China.
| | - Jingyi Li
- Department of Urology, Daping Hospital, Army Medical University, Chongqing 400042, P.R. China.
| | - Chengjie Zhong
- The Second Clinical Medical College, Chongqing Medical University, Chongqing 400016, P.R. China
| | - Yi Ling
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing 400038, P.R. China.
| | - Deng Liu
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing 400038, P.R. China.
| | - Xin Li
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing 400038, P.R. China.
| | - Jing Xu
- Department of Urology, Daping Hospital, Army Medical University, Chongqing 400042, P.R. China.
| | - Qiuli Liu
- Department of Urology, Daping Hospital, Army Medical University, Chongqing 400042, P.R. China.
| | - Yanli Guo
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing 400038, P.R. China.
| | - Luofu Wang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing 400042, P.R. China.
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23
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Wang Y, Zhou B, Ru J, Meng X, Wang Y, Liu W. Advances in computational methods for identifying cancer driver genes. Math Biosci Eng 2023; 20:21643-21669. [PMID: 38124614 DOI: 10.3934/mbe.2023958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.
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Affiliation(s)
- Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Bohao Zhou
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Jidong Ru
- School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| | - Yundong Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
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24
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Guo J, Zhang Y, Gao Y, Li S, Xu G, Tian Z, Xu Q, Li X, Li Y, Zhang Y. Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities. Artif Cells Nanomed Biotechnol 2023; 51:453-465. [PMID: 37651591 DOI: 10.1080/21691401.2023.2252477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 09/02/2023]
Abstract
Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Ya Zhang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Yueying Gao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Si Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Gang Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Zhanyu Tian
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Qi Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Xia Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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25
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Li R, Zhao H, Huang X, Zhang J, Bai R, Zhuang L, Wen S, Wu S, Zhou Q, Li M, Zeng L, Zhang S, Deng S, Su J, Zuo Z, Chen R, Lin D, Zheng J. Super-enhancer RNA m 6A promotes local chromatin accessibility and oncogene transcription in pancreatic ductal adenocarcinoma. Nat Genet 2023; 55:2224-2234. [PMID: 37957340 DOI: 10.1038/s41588-023-01568-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 10/12/2023] [Indexed: 11/15/2023]
Abstract
The biological functions of noncoding RNA N6-methyladenosine (m6A) modification remain poorly understood. In the present study, we depict the landscape of super-enhancer RNA (seRNA) m6A modification in pancreatic ductal adenocarcinoma (PDAC) and reveal a regulatory axis of m6A seRNA, H3K4me3 modification, chromatin accessibility and oncogene transcription. We demonstrate the cofilin family protein CFL1, overexpressed in PDAC, as a METTL3 cofactor that helps seRNA m6A methylation formation. The increased seRNA m6As are recognized by the reader YTHDC2, which recruits H3K4 methyltransferase MLL1 to promote H3K4me3 modification cotranscriptionally. Super-enhancers with a high level of H3K4me3 augment chromatin accessibility and facilitate oncogene transcription. Collectively, these results shed light on a CFL1-METTL3-seRNA m6A-YTHDC2/MLL1 axis that plays a role in the epigenetic regulation of local chromatin state and gene expression, which strengthens our knowledge about the functions of super-enhancers and their transcripts.
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Affiliation(s)
- Rui Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Hongzhe Zhao
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Xudong Huang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Jialiang Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Ruihong Bai
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Lisha Zhuang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Shujuan Wen
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Shaojia Wu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Quanbo Zhou
- Department of Pancreaticobiliary Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mei Li
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Lingxing Zeng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Shaoping Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Shuang Deng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Jiachun Su
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Zhixiang Zuo
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
| | - Rufu Chen
- Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Dongxin Lin
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
| | - Jian Zheng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China.
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26
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Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). Med Rev (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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27
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Zhao Y, Yin J, Zhang L, Zhang Y, Chen X. Drug-drug interaction prediction: databases, web servers and computational models. Brief Bioinform 2023; 25:bbad445. [PMID: 38113076 PMCID: PMC10782925 DOI: 10.1093/bib/bbad445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023] Open
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi 214122, China
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28
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Varshney N, Murmu S, Baral B, Kashyap D, Singh S, Kandpal M, Bhandari V, Chaurasia A, Kumar S, Jha HC. Unraveling the Aurora kinase A and Epstein-Barr nuclear antigen 1 axis in Epstein Barr virus associated gastric cancer. Virology 2023; 588:109901. [PMID: 37839162 DOI: 10.1016/j.virol.2023.109901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 09/18/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023]
Abstract
Aurora kinase A (AURKA) is one of the crucial cell cycle regulators associated with gastric cancer. Here, we explored Epstein Barr Virus-induced gastric cancer progression through EBV protein EBNA1 with AURKA. We found that EBV infection enhanced cell proliferation and migration of AGS cells and upregulation of AURKA levels. AURKA knockdown markedly reduced the proliferation and migration of the AGS cells even with EBV infection. Moreover, MD-simulation data deciphered the probable connection between EBNA1 and AURKA. The in-vitro analysis through the transcript and protein expression showed that AURKA knockdown reduces the expression of EBNA1. Moreover, EBNA1 alone can enhance AURKA protein expression in AGS cells. Co-immunoprecipitation and NMR analysis between AURKA and EBNA1 depicts the interaction between two proteins. In addition, AURKA knockdown promotes apoptosis in EBV-infected AGS cells through cleavage of Caspase-3, -9, and PARP1. This study demonstrates that EBV oncogenic modulators EBNA1 possibly modulate AURKA in EBV-mediated gastric cancer progression.
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Affiliation(s)
- Nidhi Varshney
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, India
| | - Sneha Murmu
- Division of Agricultural Bioinformatics (DABin), ICAR-Indian Agricultural Statistics Research Institute (IASRI), India
| | - Budhadev Baral
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, India
| | - Dharmendra Kashyap
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, India
| | - Siddharth Singh
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, India
| | - Meenakshi Kandpal
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, India
| | - Vasundhra Bhandari
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Hyderabad, India
| | | | - Sunil Kumar
- Division of Agricultural Bioinformatics (DABin), ICAR-Indian Agricultural Statistics Research Institute (IASRI), India.
| | - Hem Chandra Jha
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, India.
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Somers J, Fenner M, Kong G, Thirumalaisamy D, Yashar WM, Thapa K, Kinali M, Nikolova O, Babur Ö, Demir E. A framework for considering prior information in network-based approaches to omics data analysis. Proteomics 2023; 23:e2200402. [PMID: 37986684 DOI: 10.1002/pmic.202200402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 11/22/2023]
Abstract
For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.
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Affiliation(s)
- Julia Somers
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Madeleine Fenner
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Garth Kong
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Dharani Thirumalaisamy
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - William M Yashar
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Kisan Thapa
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Meric Kinali
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Olga Nikolova
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Özgün Babur
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Emek Demir
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
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Pfeifer B, Chereda H, Martin R, Saranti A, Clemens S, Hauschild AC, Beißbarth T, Holzinger A, Heider D. Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification. Bioinformatics 2023; 39:btad703. [PMID: 37988152 PMCID: PMC10684359 DOI: 10.1093/bioinformatics/btad703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/06/2023] [Accepted: 11/20/2023] [Indexed: 11/22/2023] Open
Abstract
SUMMARY Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA). AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281/zenodo.8305122).
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Affiliation(s)
- Bastian Pfeifer
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
| | - Hryhorii Chereda
- Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany
| | - Roman Martin
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
| | - Anna Saranti
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences, Vienna 1190, Austria
| | - Sandra Clemens
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
| | - Anne-Christin Hauschild
- Institute for Medical Informatics, University Medical Center Göttingen, Göttingen 37075, Germany
| | - Tim Beißbarth
- Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany
| | - Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences, Vienna 1190, Austria
| | - Dominik Heider
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
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Pollin G, De Assuncao T, Doria Jorge S, Chi YI, Charlesworth M, Madden B, Iovanna J, Zimmermann M, Urrutia R, Lomberk G. Writers and readers of H3K9me2 form distinct protein networks during the cell cycle that include candidates for H3K9 mimicry. Biosci Rep 2023; 43:BSR20231093. [PMID: 37782747 PMCID: PMC10611923 DOI: 10.1042/bsr20231093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/15/2023] [Accepted: 10/02/2023] [Indexed: 10/04/2023] Open
Abstract
Histone H3 lysine 9 methylation (H3K9me), which is written by the Euchromatic Histone Lysine Methyltransferases EHMT1 and EHMT2 and read by the heterochromatin protein 1 (HP1) chromobox (CBX) protein family, is dysregulated in many types of cancers. Approaches to inhibit regulators of this pathway are currently being evaluated for therapeutic purposes. Thus, knowledge of the complexes supporting the function of these writers and readers during the process of cell proliferation is critical for our understanding of their role in carcinogenesis. Here, we immunopurified each of these proteins and used mass spectrometry to define their associated non-histone proteins, individually and at two different phases of the cell cycle, namely G1/S and G2/M. Our findings identify novel binding proteins for these writers and readers, as well as corroborate known interactors, to show the formation of distinct protein complex networks in a cell cycle phase-specific manner. Furthermore, there is an organizational switch between cell cycle phases for interactions among specific writer-reader pairs. Through a multi-tiered bioinformatics-based approach, we reveal that many interacting proteins exhibit histone mimicry, based on an H3K9-like linear motif. Gene ontology analyses, pathway enrichment, and network reconstruction inferred that these comprehensive EHMT and CBX-associated interacting protein networks participate in various functions, including transcription, DNA repair, splicing, and membrane disassembly. Combined, our data reveals novel complexes that provide insight into key functions of cell cycle-associated epigenomic processes that are highly relevant for better understanding these chromatin-modifying proteins during cell cycle and carcinogenesis.
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Affiliation(s)
- Gareth Pollin
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI Center, Medical College of Wisconsin, Milwaukee, WI, U.S.A
| | - Thiago M. De Assuncao
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI Center, Medical College of Wisconsin, Milwaukee, WI, U.S.A
| | - Salomao Doria Jorge
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, U.S.A
| | - Young-In Chi
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI Center, Medical College of Wisconsin, Milwaukee, WI, U.S.A
| | | | - Benjamin Madden
- Medical Genome Facility, Proteomics Core, Mayo Clinic, Rochester, MN, U.S.A
| | - Juan Iovanna
- Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Parc Scientifique et Technologique de Luminy, Marseille, France
| | - Michael T. Zimmermann
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, WI, U.S.A
| | - Raul Urrutia
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI Center, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, U.S.A
| | - Gwen Lomberk
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI Center, Medical College of Wisconsin, Milwaukee, WI, U.S.A
- Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, WI, U.S.A
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Harkin EF, Nasrallah G, Le François B, Albert PR. Transcriptional Regulation of the Human 5-HT1A Receptor Gene by Lithium: Role of Deaf1 and GSK3β. Int J Mol Sci 2023; 24:15620. [PMID: 37958600 PMCID: PMC10647674 DOI: 10.3390/ijms242115620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/11/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Serotonin 1A (5-HT1A) autoreceptors located on serotonin neurons inhibit their activity, and their upregulation has been implicated in depression, suicide and resistance to antidepressant treatment. Conversely, post-synaptic 5-HT1A heteroreceptors are important for antidepressant response. The transcription factor deformed epidermal autoregulatory factor 1 (Deaf1) acts as a presynaptic repressor and postsynaptic enhancer of 5-HT1A transcription, but the mechanism is unclear. Because Deaf1 interacts with and is phosphorylated by glycogen synthase kinase 3β (GSK3β)-a constitutively active protein kinase that is inhibited by the mood stabilizer lithium at therapeutic concentrations-we investigated the role of GSK3β in Deaf1 regulation of human 5-HT1A transcription. In 5-HT1A promoter-reporter assays, human HEK293 kidney and 5-HT1A-expressing SKN-SH neuroblastoma cells, transfection of Deaf1 reduced 5-HT1A promoter activity by ~45%. To identify potential GSK3β site(s) on Deaf1, point mutations of known and predicted phosphorylation sites on Deaf1 were tested. Deaf1 repressor function was not affected by any of the mutants tested except the Y300F mutant, which augmented Deaf1 repression. Both lithium and the selective GSK3 inhibitors CHIR-99021 and AR-014418 attenuated and reversed Deaf1 repression compared to vector. This inhibition was at concentrations that maximally inhibit GSK3β activity as detected by the GSK3β-sensitive TCF/LEF reporter construct. Our results support the hypothesis that GSK3β regulates the activity of Deaf1 to repress 5-HT1A transcription and provide a potential mechanism for actions of GSK3 inhibitors on behavior.
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Affiliation(s)
| | | | | | - Paul R. Albert
- Ottawa Hospital Research Institute (Neuroscience), University of Ottawa, 451 Smyth Road, Ottawa, ON K1H-8M5, Canada (B.L.F.)
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Esmaili F, Pourmirzaei M, Ramazi S, Shojaeilangari S, Yavari E. A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction. Genomics Proteomics Bioinformatics 2023:S1672-0229(23)00110-9. [PMID: 37863385 DOI: 10.1016/j.gpb.2023.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 01/16/2023] [Accepted: 03/22/2023] [Indexed: 10/22/2023]
Abstract
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.
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Affiliation(s)
- Farzaneh Esmaili
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Mahdi Pourmirzaei
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Shahin Ramazi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115-111, Iran.
| | - Seyedehsamaneh Shojaeilangari
- Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran 33535-111, Iran
| | - Elham Yavari
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
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Niinae T, Sugiyama N, Ishihama Y. Validity of the cell-extracted proteome as a substrate pool for exploring phosphorylation motifs of kinases. Genes Cells 2023; 28:727-735. [PMID: 37658684 DOI: 10.1111/gtc.13063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/08/2023] [Accepted: 08/21/2023] [Indexed: 09/03/2023]
Abstract
Three representative protein kinases with different substrate preferences, ERK1 (Pro-directed), CK2 (acidophilic), and PKA (basophilic), were used to investigate phosphorylation sequence motifs in substrate pools consisting of the proteomes from three different cell lines, MCF7 (human mammary carcinoma), HeLa (human cervical carcinoma), and Jurkat (human acute T-cell leukemia). Specifically, recombinant kinases were added to the cell-extracted proteomes to phosphorylate the substrates in vitro. After trypsin digestion, the phosphopeptides were enriched and subjected to nanoLC/MS/MS analysis to identify their phosphorylation sites on a large scale. By analyzing the obtained phosphorylation sites and their surrounding sequences, phosphorylation motifs were extracted for each kinase-substrate proteome pair. We found that each kinase exhibited the same set of phosphorylation motifs, independently of the substrate pool proteome. Furthermore, the identified motifs were also consistent with those found using a completely randomized peptide library. These results indicate that cell-extracted proteomes can provide kinase phosphorylation motifs with sufficient accuracy, even though their sequences are not completely random, supporting the robustness of phosphorylation motif identification based on phosphoproteome analysis of cell extracts as a substrate pool for a kinase of interest.
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Affiliation(s)
- Tomoya Niinae
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Naoyuki Sugiyama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
- Laboratory of Clinical and Analytical Chemistry, National Institute of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan
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35
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He C, Qu Y, Yin J, Zhao Z, Ma R, Duan L. Cross-view contrastive representation learning approach to predicting DTIs via integrating multi-source information. Methods 2023; 218:176-188. [PMID: 37586602 DOI: 10.1016/j.ymeth.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/26/2023] [Accepted: 08/08/2023] [Indexed: 08/18/2023] Open
Abstract
Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive learning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.
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Affiliation(s)
- Chengxin He
- School of Computer Science, Sichuan University, Chengdu 610065, China; Med-X Center for Informatics, Sichuan University, Chengdu 610065, China
| | - Yuening Qu
- School of Computer Science, Sichuan University, Chengdu 610065, China
| | - Jin Yin
- The West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610065, China
| | - Zhenjiang Zhao
- School of Computer Science, Sichuan University, Chengdu 610065, China
| | - Runze Ma
- School of Computer Science, Sichuan University, Chengdu 610065, China
| | - Lei Duan
- School of Computer Science, Sichuan University, Chengdu 610065, China; Med-X Center for Informatics, Sichuan University, Chengdu 610065, China.
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Al-Aamri A, Kamarul Azman S, Daw Elbait G, Alsafar H, Henschel A. Critical assessment of on-premise approaches to scalable genome analysis. BMC Bioinformatics 2023; 24:354. [PMID: 37735350 PMCID: PMC10512525 DOI: 10.1186/s12859-023-05470-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Plummeting DNA sequencing cost in recent years has enabled genome sequencing projects to scale up by several orders of magnitude, which is transforming genomics into a highly data-intensive field of research. This development provides the much needed statistical power required for genotype-phenotype predictions in complex diseases. METHODS In order to efficiently leverage the wealth of information, we here assessed several genomic data science tools. The rationale to focus on on-premise installations is to cope with situations where data confidentiality and compliance regulations etc. rule out cloud based solutions. We established a comprehensive qualitative and quantitative comparison between BCFtools, SnpSift, Hail, GEMINI, and OpenCGA. The tools were compared in terms of data storage technology, query speed, scalability, annotation, data manipulation, visualization, data output representation, and availability. RESULTS Tools that leverage sophisticated data structures are noted as the most suitable for large-scale projects in varying degrees of scalability in comparison to flat-file manipulation (e.g., BCFtools, and SnpSift). Remarkably, for small to mid-size projects, even lightweight relational database. CONCLUSION The assessment criteria provide insights into the typical questions posed in scalable genomics and serve as guidance for the development of scalable computational infrastructure in genomics.
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Affiliation(s)
- Amira Al-Aamri
- Department of Electrical Engineering and Computer Science, College of Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Syafiq Kamarul Azman
- Department of Electrical Engineering and Computer Science, College of Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Gihan Daw Elbait
- Department of Biology, College of Arts and Sciences, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
- Center for Biotechnology (BTC), Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology (BTC), Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Andreas Henschel
- Department of Electrical Engineering and Computer Science, College of Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
- Center for Biotechnology (BTC), Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
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Pan Y, Ren H, Lan L, Li Y, Huang T. Review of Predicting Synergistic Drug Combinations. Life (Basel) 2023; 13:1878. [PMID: 37763281 PMCID: PMC10533134 DOI: 10.3390/life13091878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.
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Affiliation(s)
- Yichen Pan
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Liang Lan
- Department of Interactive Media, Hong Kong Baptist University, Hong Kong, China;
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
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Yao K, Wang X, Li W, Zhu H, Jiang Y, Li Y, Tian T, Yang Z, Liu Q, Liu Q. Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction. Comput Biol Med 2023; 163:107199. [PMID: 37421738 DOI: 10.1016/j.compbiomed.2023.107199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/15/2023] [Accepted: 06/19/2023] [Indexed: 07/10/2023]
Abstract
Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.
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Affiliation(s)
- Kainan Yao
- School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China
| | - Xiaowen Wang
- School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China
| | - Wannian Li
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Yangpu District, Shanghai, 200092, China.
| | - Hongming Zhu
- School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China
| | - Yizhi Jiang
- School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China
| | - Yulong Li
- School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China
| | - Tongxuan Tian
- School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China
| | - Zhaoyi Yang
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Hefei, 230001, Anhui, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Yangpu District, Shanghai, 200092, China.
| | - Qin Liu
- School of Software Engineering, Tongji University, 4800 Caoan Road, Jiading District, Shanghai, 201804, China.
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Luo X, Wang L, Hu P, Hu L. Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder. IEEE/ACM Trans Comput Biol Bioinform 2023; 20:3182-3194. [PMID: 37155405 DOI: 10.1109/tcbb.2023.3273567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.
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Chen J, Zhang L, Cheng K, Jin B, Lu X, Che C. Predicting Drug-Target Interaction Via Self-Supervised Learning. IEEE/ACM Trans Comput Biol Bioinform 2023; 20:2781-2789. [PMID: 35230952 DOI: 10.1109/tcbb.2022.3153963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. However, it still suffers from deficiencies of dependence on manual labels and vulnerability to attacks. Inspired by the success of self-supervised learning (SSL) algorithms, which can leverage input data itself as supervision,we propose SupDTI, a SSL-enhanced drug-target interaction prediction framework based on a heterogeneous network (i.e., drug-protein, drug-drug, and protein-protein interaction network; drug-disease, drug-side-effect, and protein-disease association network; drug-structure and protein-sequence similarity network). Specifically, SupDTI is an end-to-end learning framework consisting of five components. First, localized and globalized graph convolutions are designed to capture the nodes' information from both local and global perspectives, respectively. Then, we develop a variational autoencoder to constrain the nodes' representation to have desired statistical characteristics. Finally, a unified self-supervised learning strategy is leveraged to enhance the nodes' representation, namely, a contrastive learning module is employed to enable the nodes' representation to fit the graph-level representation, followed by a generative learning module which further maximizes the node-level agreement across the global and local views by learning the probabilistic connectivity distribution of the original heterogeneous network. Experimental results show that our model can achieve better prediction performance than state-of-the-art methods.
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Wang W, Meng X, Xiang J, Shuai Y, Bedru HD, Li M. CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes. IEEE J Biomed Health Inform 2023; 27:4569-4578. [PMID: 37399160 DOI: 10.1109/jbhi.2023.3289490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes.
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Zhao BW, Su XR, Hu PW, Huang YA, You ZH, Hu L. iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network. Bioinformatics 2023; 39:btad451. [PMID: 37505483 PMCID: PMC10397422 DOI: 10.1093/bioinformatics/btad451] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 06/12/2023] [Accepted: 07/27/2023] [Indexed: 07/29/2023] Open
Abstract
MOTIVATION The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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Athanasiadis P, Ravikumar B, Elliott RJ, Dawson JC, Carragher NO, Clemons PA, Johanssen T, Ebner D, Aittokallio T. Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells. iScience 2023; 26:107209. [PMID: 37485377 PMCID: PMC10359939 DOI: 10.1016/j.isci.2023.107209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/21/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.
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Affiliation(s)
- Paschalis Athanasiadis
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
| | - Balaguru Ravikumar
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
| | - Richard J.R. Elliott
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C. Dawson
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O. Carragher
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Paul A. Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, United States
| | - Timothy Johanssen
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Daniel Ebner
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Tero Aittokallio
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
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Gutiérrez-Casares JR, Segú-Vergés C, Sabate Chueca J, Pozo-Rubio T, Coma M, Montoto C, Quintero J. In silico evaluation of the role of lisdexamfetamine on attention-deficit/hyperactivity disorder common psychiatric comorbidities: mechanistic insights on binge eating disorder and depression. Front Neurosci 2023; 17:1118253. [PMID: 37457000 PMCID: PMC10347683 DOI: 10.3389/fnins.2023.1118253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a psychiatric condition well recognized in the pediatric population that can persist into adulthood. The vast majority of patients with ADHD present psychiatric comorbidities that have been suggested to share, to some extent, the pathophysiological mechanism of ADHD. Lisdexamfetamine (LDX) is a stimulant prodrug approved for treating ADHD and, in the US, also for binge eating disorder (BED). Herein, we evaluated, through a systems biology-based in silico method, the efficacy of a virtual model of LDX (vLDX) as ADHD treatment to improve five common ADHD psychiatric comorbidities in adults and children, and we explored the molecular mechanisms behind LDX's predicted efficacy. After the molecular characterization of vLDX and the comorbidities (anxiety, BED, bipolar disorder, depression, and tics disorder), we created a protein-protein interaction human network to which we applied artificial neural networks (ANN) algorithms. We also generated virtual populations of adults and children-adolescents totaling 2,600 individuals and obtained the predicted protein activity from Therapeutic Performance Mapping System models. The latter showed that ADHD molecular description shared 53% of its protein effectors with at least one studied psychiatric comorbidity. According to the ANN analysis, proteins targeted by vLDX are predicted to have a high probability of being related to BED and depression. In BED, vLDX was modeled to act upon neurotransmission and neuroplasticity regulators, and, in depression, vLDX regulated the hypothalamic-pituitary-adrenal axis, neuroinflammation, oxidative stress, and glutamatergic excitotoxicity. In conclusion, our modeling results, despite their limitations and although requiring in vitro or in vivo validation, could supplement the design of preclinical and potentially clinical studies that investigate treatment for patients with ADHD with psychiatric comorbidities, especially from a molecular point of view.
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Affiliation(s)
- José Ramón Gutiérrez-Casares
- Unidad Ambulatoria de Psiquiatría y Salud Mental de la Infancia, Niñez y Adolescencia, Hospital Perpetuo Socorro, Badajoz, Spain
| | - Cristina Segú-Vergés
- Anaxomics Biotech, Barcelona, Spain
- Research Programme on Biomedical Informatics (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
| | | | | | | | - Carmen Montoto
- Department of Medical, Takeda Farmacéutica España, Madrid, Spain
| | - Javier Quintero
- Servicio de Psiquiatría, Hospital Universitario Infanta Leonor, Departamento de Medicina Legal, Patología y Psiquiatría, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
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Veenstra BT, Veenstra TD. Proteomic applications in identifying protein-protein interactions. Adv Protein Chem Struct Biol 2023; 138:1-48. [PMID: 38220421 DOI: 10.1016/bs.apcsb.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.
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Affiliation(s)
- Benjamin T Veenstra
- Department of Math and Sciences, Cedarville University, Cedarville, OH, United States
| | - Timothy D Veenstra
- School of Pharmacy, Cedarville University, Cedarville, OH, United States.
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Madugula SS, Pandey S, Amalapurapu S, Bozdag S. NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool. ACS Omega 2023; 8:20379-20388. [PMID: 37323377 PMCID: PMC10268018 DOI: 10.1021/acsomega.3c00286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023]
Abstract
The nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways. Existing NR prediction tools only use a few types of sequence-based features and are tested on relatively similar independent datasets; thus, they may suffer from overfitting when extended to new genera of sequences. To address this problem, we developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized. The first level of NRPreTo allows for the successful prediction of a query protein as NR or non-NR and further subclassifies the protein into one of the seven NR subfamilies in the second level. We developed Random Forest classifiers to test on benchmark datasets, as well as the entire human protein datasets from RefSeq and Human Protein Reference Database (HPRD). We observed that using additional feature groups improved the performance. We also observed that NRPreTo achieved high performance on the external datasets and predicted 59 novel NRs in the human proteome. The source code of NRPreTo is publicly available at https://github.com/bozdaglab/NRPreTo.
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Affiliation(s)
- Sita Sirisha Madugula
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
| | - Suman Pandey
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
| | - Shreya Amalapurapu
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
- The
Texas Academy of Mathematics and Science, University of North Texas, Denton, Texas TX 76203, United States
| | - Serdar Bozdag
- Department
of Computer Science & Engineering, University
of North Texas, Denton, Texas TX 76203, United States
- Department
of Mathematics, University of North Texas, Denton, Texas TX 76203, United
States
- BioDiscovery
Institute, University of North Texas, Denton, Texas TX 76203, United States
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Mo J, Li Z, Chen H, Lu Z, Ding B, Yuan X, Liu Y, Zhu W. Network medicine framework identified drug-repurposing opportunities of pharmaco-active compounds of Angelica acutiloba (Siebold & Zucc.) Kitag. for skin aging. Aging (Albany NY) 2023; 15:5144-5163. [PMID: 37310405 PMCID: PMC10292898 DOI: 10.18632/aging.204789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/15/2023] [Indexed: 06/14/2023]
Abstract
Increasing incidence of skin aging has highlighted the importance of identifying effective drugs with repurposed opportunities for skin aging. We aimed to identify pharmaco-active compounds with drug-repurposing opportunities for skin aging from Angelica acutiloba (Siebold & Zucc.) Kitag. (AAK). The proximity of network medicine framework (NMF) firstly identified 8 key AAK compounds with repurposed opportunities for skin aging, which may exert by regulating 29 differentially expressed genes (DGEs) of skin aging, including 13 up-regulated targets and 16 down-regulated targets. Connectivity MAP (cMAP) analysis revealed 8 key compounds were involved in regulating the process of cell proliferation and apoptosis, mitochondrial energy metabolism and oxidative stress of skin aging. Molecular docking analysis showed that 8 key compounds had a high docked ability with AR, BCHE, HPGD and PI3, which were identified as specific biomarker for the diagnosis of skin aging. Finally, the mechanisms of these key compounds were predicted to be involved in inhibiting autophagy pathway and activating Phospholipase D signaling pathway. In conclusion, this study firstly elucidated the drug-repurposing opportunities of AAK compounds for skin aging, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.
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Affiliation(s)
- Jiaxin Mo
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Zunjiang Li
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Hankun Chen
- Guangzhou Qinglan Biotechnology Co. Ltd., Guangzhou Province 515000, China
| | - Zhongyu Lu
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Banghan Ding
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
| | - Xiaohong Yuan
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
| | - Yuan Liu
- Guangzhou Huamiao Biotechnology Research Institute Co. Ltd., Guangzhou Province 510000, China
| | - Wei Zhu
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
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Zhao L, Zhang H, Li N, Chen J, Xu H, Wang Y, Liang Q. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. J Ethnopharmacol 2023; 309:116306. [PMID: 36858276 DOI: 10.1016/j.jep.2023.116306] [Citation(s) in RCA: 71] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/06/2023] [Accepted: 02/19/2023] [Indexed: 05/20/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds. AIM OF THE STUDY The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine. MATERIALS AND METHODS Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022. RESULTS Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend. CONCLUSION Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.
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Affiliation(s)
- Li Zhao
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hong Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Ning Li
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Jinman Chen
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hao Xu
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Yongjun Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
| | - Qianqian Liang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
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Zabihian A, Sayyad FZ, Hashemi SM, Shami Tanha R, Hooshmand M, Gharaghani S. DEDTI versus IEDTI: efficient and predictive models of drug-target interactions. Sci Rep 2023; 13:9238. [PMID: 37286613 DOI: 10.1038/s41598-023-36438-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/03/2023] [Indexed: 06/09/2023] Open
Abstract
Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representations. The IEDTI takes advantage of triplet and maps the input accumulated similarity features into meaningful embedding corresponding vectors. Then, it applies a deep predictive model to each drug-target pair to evaluate their interaction. The DEDTI directly uses the accumulated similarity feature vectors of drugs and targets and applies a predictive model on each pair to identify their interactions. We have done a comprehensive simulation on the DTINet dataset as well as gold standard datasets, and the results show that DEDTI outperforms IEDTI and the state-of-the-art models. In addition, we conduct a docking study on new predicted interactions between two drug-target pairs, and the results confirm acceptable drug-target binding affinity between both predicted pairs.
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Affiliation(s)
- Arash Zabihian
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, Iran
| | - Faeze Zakaryapour Sayyad
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Seyyed Morteza Hashemi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Reza Shami Tanha
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Mohsen Hooshmand
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Misetic H, Keddar MR, Jeannon JP, Ciccarelli FD. Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment. Genome Med 2023; 15:40. [PMID: 37277866 DOI: 10.1186/s13073-023-01197-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood. METHODS Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with. RESULTS The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. CONCLUSIONS Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .
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Affiliation(s)
- Hrvoje Misetic
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK
| | - Mohamed Reda Keddar
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK
| | - Jean-Pierre Jeannon
- Department of Head & Neck Surgery, Great Maze Pond, Guy's Hospital, London, SE1 9RT, UK
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK.
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK.
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