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Datta C, Das P, Dutta S, Prasad T, Banerjee A, Gehlot S, Ghosal A, Dhabal S, Biswas P, De D, Chaudhuri S, Bhattacharjee A. AMPK activation reduces cancer cell aggressiveness via inhibition of monoamine oxidase A (MAO-A) expression/activity. Life Sci 2024; 352:122857. [PMID: 38914305 DOI: 10.1016/j.lfs.2024.122857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/14/2024] [Accepted: 06/16/2024] [Indexed: 06/26/2024]
Abstract
AIM AMPK can be considered as an important target molecule for cancer for its unique ability to directly recognize cellular energy status. The main aim of this study is to explore the role of different AMPK activators in managing cancer cell aggressiveness and to understand the mechanistic details behind the process. MAIN METHODS First, we explored the AMPK expression pattern and its significance in different subtypes of lung cancer by accessing the TCGA data sets for LUNG, LUAD and LUSC patients and then established the correlation between AMPK expression pattern and overall survival of lung cancer patients using Kaplan-Meire plot. We further carried out several cell-based assays by employing different wet lab techniques including RT-PCR, Western Blot, proliferation, migration and invasion assays to fulfil the aim of the study. KEY FINDINGS SIGNIFICANCE: This study identifies the importance of AMPK activators as a repurposing agent for combating lung and colon cancer cell aggressiveness. It also suggests SRT-1720 as a potent repurposing agent for cancer treatment especially in NSCLC patients where a point mutation is present in LKB1.
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Affiliation(s)
- Chandreyee Datta
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Payel Das
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Subhajit Dutta
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Tuhina Prasad
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Abhineet Banerjee
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Sameep Gehlot
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Arpa Ghosal
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Sukhamoy Dhabal
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Pritam Biswas
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Debojyoti De
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Surabhi Chaudhuri
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Ashish Bhattacharjee
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India.
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2
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Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Li H, Xu J, Brendel M, Maasch JRMA, Bai Z, Zhang H, Zhu Y, Cincotta MC, Shi X, Henchcliffe C, Leverenz JB, Cummings J, Okun MS, Bian J, Cheng F, Wang F. Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 2024; 7:184. [PMID: 38982243 PMCID: PMC11233682 DOI: 10.1038/s41746-024-01175-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
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Grants
- R21 AG083003 NIA NIH HHS
- R01 AG082118 NIA NIH HHS
- R56 AG074001 NIA NIH HHS
- R01AG076448 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1AG072449 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- MJFF-023081 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- R01AG080991 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P30 AG072959 NIA NIH HHS
- 3R01AG066707-01S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21AG083003 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG066707 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 AG071476 NIA NIH HHS
- RF1 AG082211 NIA NIH HHS
- R56AG074001 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG082118 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R25 AG083721 NIA NIH HHS
- RF1AG082211 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 NS093334 NINDS NIH HHS
- AG083721-01 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1NS133812 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM109025 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 NS133812 NINDS NIH HHS
- R35AG71476 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 AG073323 NIA NIH HHS
- R01 AG066707 NIA NIH HHS
- R01AG053798 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG076234 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01 AG076448 NIA NIH HHS
- R01 AG080991 NIA NIH HHS
- R01 AG076234 NIA NIH HHS
- U01NS093334 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20 GM109025 NIGMS NIH HHS
- P30AG072959 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 AG072449 NIA NIH HHS
- R01 AG053798 NIA NIH HHS
- 3R01AG066707-02S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01AG073323 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- ALZDISCOVERY-1051936 Alzheimer's Association
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Manqi Zhou
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Alison Ke
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Haoyang Li
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jacqueline R M A Maasch
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computer Science, Cornell Tech, Cornell University, New York, NY, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
| | - Molly C Cincotta
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Claire Henchcliffe
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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3
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Gou Y, Liu D, Chen M, Wei Y, Huang X, Han C, Feng Z, Zhang C, Lu T, Peng D, Xue Y. GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs. Nucleic Acids Res 2024; 52:W238-W247. [PMID: 38709873 PMCID: PMC11223847 DOI: 10.1093/nar/gkae346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
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Affiliation(s)
- Yujie Gou
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Dan Liu
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Miaomiao Chen
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yuxiang Wei
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xinhe Huang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Cheng Han
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Zihao Feng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Chi Zhang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Teng Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing100190, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing210031, China
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4
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Gualdi F, Oliva B, Piñero J. Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information. NAR Genom Bioinform 2024; 6:lqae049. [PMID: 38745993 PMCID: PMC11091931 DOI: 10.1093/nargab/lqae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/08/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024] Open
Abstract
Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.
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Affiliation(s)
- Francesco Gualdi
- Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (IBI-GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
- Structural Bioinformatics Lab, Research Programme on Biomedical Informatics (SBI-GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
| | - Baldomero Oliva
- Structural Bioinformatics Lab, Research Programme on Biomedical Informatics (SBI-GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
| | - Janet Piñero
- Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (IBI-GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
- Medbioinformatics Solutions SL, Barcelona, Spain
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5
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Sanjeev D, George M, John L, Gopalakrishnan AP, Priyanka P, Mendon S, Yandigeri T, Nisar M, Nisar M, Kanekar S, Balaya RDA, Raju R. Tyr352 as a Predominant Phosphosite in the Understudied Kinase and Molecular Target, HIPK1: Implications for Cancer Therapy. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:111-124. [PMID: 38498023 DOI: 10.1089/omi.2023.0244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Homeodomain-interacting protein kinase 1 (HIPK1) is majorly found in the nucleoplasm. HIPK1 is associated with cell proliferation, tumor necrosis factor-mediated cellular apoptosis, transcription regulation, and DNA damage response, and thought to play significant roles in health and common diseases such as cancer. Despite this, HIPK1 remains an understudied molecular target. In the present study, based on a systematic screening and mapping approach, we assembled 424 qualitative and 44 quantitative phosphoproteome datasets with 15 phosphosites in HIPK1 reported across multiple studies. These HIPK1 phosphosites were not currently attributed to any functions. Among them, Tyr352 within the kinase domain was identified as the predominant phosphosite modulated in 22 differential datasets. To analyze the functional association of HIPK1 Tyr352, we first employed a stringent criterion to derive its positively and negatively correlated protein phosphosites. Subsequently, we categorized the correlated phosphosites in known interactors, known/predicted kinases, and substrates of HIPK1, for their prioritized validation. Bioinformatics analysis identified their significant association with biological processes such as the regulation of RNA splicing, DNA-templated transcription, and cellular metabolic processes. HIPK1 Tyr352 was also identified to be upregulated in Her2+ cell lines and a subset of pancreatic and cholangiocarcinoma tissues. These data and the systems biology approach undertaken in the present study serve as a platform to explore the functional role of other phosphosites in HIPK1, and by extension, inform cancer drug discovery and oncotherapy innovation. In all, this study highlights the comprehensive phosphosite map of HIPK1 kinase and the first of its kind phosphosite-centric analysis of HIPK1 kinase based on global-level phosphoproteomics datasets derived from human cellular differential experiments across distinct experimental conditions.
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Affiliation(s)
- Diya Sanjeev
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | - Mejo George
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | | | - Pahal Priyanka
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | - Spoorthi Mendon
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | - Tanuja Yandigeri
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | - Mahammad Nisar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | - Muhammad Nisar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | - Saptami Kanekar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
| | | | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed-to-be University), Mangalore, Karnataka, India
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Tang R, Sun C, Huang J, Li M, Wei J, Liu J. Predicting Drug-Protein Interactions by Self-Adaptively Adjusting the Topological Structure of the Heterogeneous Network. IEEE J Biomed Health Inform 2023; 27:5675-5684. [PMID: 37672364 DOI: 10.1109/jbhi.2023.3312374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.
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7
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Gilanchi S, Faranoush M, Daskareh M, Sadjjadi FS, Zali H, Ghassempour A, Rezaei Tavirani M. Proteomic-Based Discovery of Predictive Biomarkers for Drug Therapy Response and Personalized Medicine in Chronic Immune Thrombocytopenia. BIOMED RESEARCH INTERNATIONAL 2023; 2023:9573863. [PMID: 37942029 PMCID: PMC10630023 DOI: 10.1155/2023/9573863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/17/2023] [Accepted: 09/30/2023] [Indexed: 11/10/2023]
Abstract
Purpose ITP is the most prevalent autoimmune blood disorder. The lack of predictive biomarkers for therapeutic response is a major challenge for physicians caring of chronic ITP patients. This study is aimed at identifying predictive biomarkers for drug therapy responses. Methods 2D gel electrophoresis (2-DE) was performed to find differentially expressed proteins. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) analysis was performed to identify protein spots. The Cytoscape software was employed to visualize and analyze the protein-protein interaction (PPI) network. Then, enzyme-linked immunosorbent assays (ELISA) were used to confirm the results of the proteins detected in the blood. The DAVID online software was used to explore the Gene Ontology and pathways involved in the disease. Results Three proteins, including APOA1, GC, and TF, were identified as hub-bottlenecks and confirmed by ELISA. Enrichment analysis results showed the importance of several biological processes and pathway, such as the PPAR signaling pathway, complement and coagulation cascades, platelet activation, vitamin digestion and absorption, fat digestion and absorption, cell adhesion molecule binding, and receptor binding. Conclusion and Clinical Relevance. Our results indicate that plasma proteins (APOA1, GC, and TF) can be suitable biomarkers for the prognosis of the response to drug therapy in ITP patients.
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Affiliation(s)
- Samira Gilanchi
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Faranoush
- Pediatric Growth and Development Research Center, Institute of Endocrinology, Iran University of Medical Sciences, Tehran, Iran
| | - Mahyar Daskareh
- Department of Radiology, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Sadat Sadjjadi
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hakimeh Zali
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Ghassempour
- Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G.C., Evin, Tehran, Iran
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8
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Gowthami N, Pursotham N, Dey G, Ghose V, Sathe G, Pruthi N, Shukla D, Gayathri N, Santhoshkumar R, Padmanabhan B, Chandramohan V, Mahadevan A, Srinivas Bharath MM. Neuroanatomical zones of human traumatic brain injury reveal significant differences in protein profile and protein oxidation: Implications for secondary injury events. J Neurochem 2023; 167:218-247. [PMID: 37694499 DOI: 10.1111/jnc.15953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/03/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023]
Abstract
Traumatic brain injury (TBI) causes significant neurological deficits and long-term degenerative changes. Primary injury in TBI entails distinct neuroanatomical zones, i.e., contusion (Ct) and pericontusion (PC). Their dynamic expansion could contribute to unpredictable neurological deterioration in patients. Molecular characterization of these zones compared with away from contusion (AC) zone is invaluable for TBI management. Using proteomics-based approach, we were able to distinguish Ct, PC and AC zones in human TBI brains. Ct was associated with structural changes (blood-brain barrier (BBB) disruption, neuroinflammation, axonal injury, demyelination and ferroptosis), while PC was associated with initial events of secondary injury (glutamate excitotoxicity, glial activation, accumulation of cytoskeleton proteins, oxidative stress, endocytosis) and AC displayed mitochondrial dysfunction that could contribute to secondary injury events and trigger long-term degenerative changes. Phosphoproteome analysis in these zones revealed that certain differentially phosphorylated proteins synergistically contribute to the injury events along with the differentially expressed proteins. Non-synaptic mitochondria (ns-mito) was associated with relatively more differentially expressed proteins (DEPs) compared to synaptosomes (Syn), while the latter displayed increased protein oxidation including tryptophan (Trp) oxidation. Proteomic analysis of immunocaptured complex I (CI) from Syn revealed increased Trp oxidation in Ct > PC > AC (vs. control). Oxidized W272 in the ND1 subunit of CI, revealed local conformational changes in ND1 and the neighboring subunits, as indicated by molecular dynamics simulation (MDS). Taken together, neuroanatomical zones in TBI show distinct protein profile and protein oxidation representing different primary and secondary injury events with potential implications for TBI pathology and neurological status of the patients.
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Affiliation(s)
- Niya Gowthami
- Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Nithya Pursotham
- Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Gourav Dey
- Proteomics and Bioinformatics Laboratory, Neurobiology Research Center, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
- Institute of Bioinformatics, Bengaluru, India
| | - Vivek Ghose
- Proteomics and Bioinformatics Laboratory, Neurobiology Research Center, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
- Institute of Bioinformatics, Bengaluru, India
| | - Gajanan Sathe
- Proteomics and Bioinformatics Laboratory, Neurobiology Research Center, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
- Institute of Bioinformatics, Bengaluru, India
| | - Nupur Pruthi
- Department of Neurosurgery, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Dhaval Shukla
- Department of Neurosurgery, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Narayanappa Gayathri
- Department of Neuropathology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Rashmi Santhoshkumar
- Department of Neuropathology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Balasundaram Padmanabhan
- Department of Biophysics, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Vivek Chandramohan
- Department of Biotechnology, Siddaganga Institute of Technology (SIT), Tumakuru, India
| | - Anita Mahadevan
- Department of Neuropathology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - M M Srinivas Bharath
- Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
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9
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Jiang Y, Li J, Liu Y, Shen X, Li J, Zhi F, Xu J, Li X, Shao T, Xu Y. Open a new epoch of arsenic trioxide investigation: ATOdb. Comput Biol Med 2023; 165:107465. [PMID: 37699323 DOI: 10.1016/j.compbiomed.2023.107465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023]
Abstract
Arsenic trioxide (ATO) is a great discovery in the treatment of acute promyelocytic leukemia (APL), which has been used in an increasing number of malignant diseases. Systematic integrative analysis will help to precisely understand the mechanism of ATO and find new combined drugs. Therefore, we developed a one-stop comprehensive database of ATO named ATOdb by manually compiling a wealth of experimentally supported ATO-related data from 3479 articles, and integrated analysis tools. The current version of ATOdb contains 8373 associations among 2300 ATO targets, 80 conditions and 262 combined drugs. Each entry in ATOdb contains detailed information on ATO targets, therapeutic/side effects, systems, cell names, cell types, regulations, detection methods, brief descriptions, references, etc. Furthermore, ATOdb also provides data visualization and analysis results such as the drug similarities, protein-protein interactions, and miRNA-mRNA relationships. An easy-to-use web interface was deployed in ATOdb for users to easily browse, search and download the data. In conclusion, ATOdb will serve as a valuable resource for in-depth study of the mechanism of ATO, discovery of new drug combination strategies, promotion of rational drug use and individualized treatments. ATOdb is freely available at http://bio-bigdata.hrbmu.edu.cn/ATOdb/index.jsp.
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Affiliation(s)
- Yanan Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, China; Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin 150081, China
| | - Jianing Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yujie Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiuyun Shen
- Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, China
| | - Junyi Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Fengnan Zhi
- Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tingting Shao
- 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; Hohhot Mongolian Medicine of Traditional Chinese Medicine Hospital, Hohhot, 010110, China.
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10
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Zhu J, Oh JH, Simhal AK, Elkin R, Norton L, Deasy JO, Tannenbaum A. Geometric graph neural networks on multi-omics data to predict cancer survival outcomes. Comput Biol Med 2023; 163:107117. [PMID: 37329617 PMCID: PMC10638676 DOI: 10.1016/j.compbiomed.2023.107117] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/19/2023]
Abstract
The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational methods to analyze large-scale, high-dimensional genomic data. However, traditional machine learning methods face a challenge in handling the high-dimensional, low-sample size problem that is shown in most genomic data sets. To address this, our group has developed geometric network analysis techniques on multi-omics data in connection with prior biological knowledge derived from protein-protein interactions (PPIs) or pathways. Geometric features obtained from the genomic network, such as Ollivier-Ricci curvature and the invariant measure of the associated Markov chain, have been shown to be predictive of survival outcomes in various cancers. In this study, we propose a novel supervised deep learning method called geometric graph neural network (GGNN) that incorporates such geometric features into deep learning for enhanced predictive power and interpretability. More specifically, we utilize a state-of-the-art graph neural network with sparse connections between the hidden layers based on known biology of the PPI network and pathway information. Geometric features along with multi-omics data are then incorporated into the corresponding layers. The proposed approach utilizes a local-global principle in such a manner that highly predictive features are selected at the front layers and fed directly to the last layer for multivariable Cox proportional-hazards regression modeling. The method was applied to multi-omics data from the CoMMpass study of multiple myeloma and ten major cancers in The Cancer Genome Atlas (TCGA). In most experiments, our method showed superior predictive performance compared to other alternative methods.
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Affiliation(s)
- Jiening Zhu
- Department of Applied Mathematics & Statistics, Stony Brook University, NY, USA.
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Anish K Simhal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Rena Elkin
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Larry Norton
- Department of Medicine, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Allen Tannenbaum
- Department of Applied Mathematics & Statistics, Stony Brook University, NY, USA; Department of Computer Science, Stony Brook University, NY, USA.
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11
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Shimada MK. Splicing Modulators Are Involved in Human Polyglutamine Diversification via Protein Complexes Shuttling between Nucleus and Cytoplasm. Int J Mol Sci 2023; 24:ijms24119622. [PMID: 37298574 DOI: 10.3390/ijms24119622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Length polymorphisms of polyglutamine (polyQs) in triplet-repeat-disease-causing genes have diversified during primate evolution despite them conferring a risk of human-specific diseases. To explain the evolutionary process of this diversification, there is a need to focus on mechanisms by which rapid evolutionary changes can occur, such as alternative splicing. Proteins that can bind polyQs are known to act as splicing factors and may provide clues about the rapid evolutionary process. PolyQs are also characterized by the formation of intrinsically disordered (ID) regions, so I hypothesized that polyQs are involved in the transportation of various molecules between the nucleus and cytoplasm to regulate mechanisms characteristic of humans such as neural development. To determine target molecules for empirical research to understand the evolutionary change, I explored protein-protein interactions (PPIs) involving the relevant proteins. This study identified pathways related to polyQ binding as hub proteins scattered across various regulatory systems, including regulation via PQBP1, VCP, or CREBBP. Nine ID hub proteins with both nuclear and cytoplasmic localization were found. Functional annotations suggested that ID proteins containing polyQs are involved in regulating transcription and ubiquitination by flexibly changing PPI formation. These findings explain the relationships among splicing complex, polyQ length variations, and modifications in neural development.
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Affiliation(s)
- Makoto K Shimada
- Center for Medical Science, Fujita Health University, Toyoake 470-1192, Japan
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12
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Bhin J, Paes Dias M, Gogola E, Rolfs F, Piersma SR, de Bruijn R, de Ruiter JR, van den Broek B, Duarte AA, Sol W, van der Heijden I, Andronikou C, Kaiponen TS, Bakker L, Lieftink C, Morris B, Beijersbergen RL, van de Ven M, Jimenez CR, Wessels LFA, Rottenberg S, Jonkers J. Multi-omics analysis reveals distinct non-reversion mechanisms of PARPi resistance in BRCA1- versus BRCA2-deficient mammary tumors. Cell Rep 2023; 42:112538. [PMID: 37209095 DOI: 10.1016/j.celrep.2023.112538] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/16/2023] [Accepted: 05/03/2023] [Indexed: 05/22/2023] Open
Abstract
BRCA1 and BRCA2 both function in DNA double-strand break repair by homologous recombination (HR). Due to their HR defect, BRCA1/2-deficient cancers are sensitive to poly(ADP-ribose) polymerase inhibitors (PARPis), but they eventually acquire resistance. Preclinical studies yielded several PARPi resistance mechanisms that do not involve BRCA1/2 reactivation, but their relevance in the clinic remains elusive. To investigate which BRCA1/2-independent mechanisms drive spontaneous resistance in vivo, we combine molecular profiling with functional analysis of HR of matched PARPi-naive and PARPi-resistant mouse mammary tumors harboring large intragenic deletions that prevent reactivation of BRCA1/2. We observe restoration of HR in 62% of PARPi-resistant BRCA1-deficient tumors but none in the PARPi-resistant BRCA2-deficient tumors. Moreover, we find that 53BP1 loss is the prevalent resistance mechanism in HR-proficient BRCA1-deficient tumors, whereas resistance in BRCA2-deficient tumors is mainly induced by PARG loss. Furthermore, combined multi-omics analysis identifies additional genes and pathways potentially involved in modulating PARPi response.
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Affiliation(s)
- Jinhyuk Bhin
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands; Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands; Department of Biomedical System Informatics, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Mariana Paes Dias
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Ewa Gogola
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Frank Rolfs
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands; OncoProteomics Laboratory, Department Medical Oncology, Amsterdam UMC, 1081HV Amsterdam, the Netherlands
| | - Sander R Piersma
- OncoProteomics Laboratory, Department Medical Oncology, Amsterdam UMC, 1081HV Amsterdam, the Netherlands
| | - Roebi de Bruijn
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands; Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Julian R de Ruiter
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands; Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Bram van den Broek
- Division of Cell Biology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Alexandra A Duarte
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Wendy Sol
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Ingrid van der Heijden
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Christina Andronikou
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands; Cancer Therapy Resistance Cluster and Bern Center for Precision Medicine, Department for Biomedical Research, University of Bern, 3088 Bern, Switzerland; Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
| | - Taina S Kaiponen
- Cancer Therapy Resistance Cluster and Bern Center for Precision Medicine, Department for Biomedical Research, University of Bern, 3088 Bern, Switzerland; Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
| | - Lara Bakker
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Cor Lieftink
- Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Ben Morris
- Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Roderick L Beijersbergen
- Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Marieke van de Ven
- Mouse Clinic for Cancer and Aging, Preclinical Intervention Unit, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands
| | - Connie R Jimenez
- OncoProteomics Laboratory, Department Medical Oncology, Amsterdam UMC, 1081HV Amsterdam, the Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands.
| | - Sven Rottenberg
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands; Cancer Therapy Resistance Cluster and Bern Center for Precision Medicine, Department for Biomedical Research, University of Bern, 3088 Bern, Switzerland; Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.
| | - Jos Jonkers
- Division of Molecular Pathology, Oncode Institute, the Netherlands Cancer Institute, 1066CX Amsterdam, the Netherlands.
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13
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Xu J, Xu J, Meng Y, Lu C, Cai L, Zeng X, Nussinov R, Cheng F. Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data. CELL REPORTS METHODS 2023; 3:100382. [PMID: 36814845 PMCID: PMC9939381 DOI: 10.1016/j.crmeth.2022.100382] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/31/2022] [Accepted: 12/08/2022] [Indexed: 05/25/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.
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Affiliation(s)
- Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Changcheng Lu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Lijun Cai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - 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
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14
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Bhardwaj T, Ahmad I, Somvanshi P. Systematic analysis to identify novel disease indications and plausible potential chemical leads of glutamate ionotropic receptor NMDA type subunit 1, GRIN1. J Mol Recognit 2023; 36:e2997. [PMID: 36259267 DOI: 10.1002/jmr.2997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/19/2022] [Accepted: 10/07/2022] [Indexed: 12/15/2022]
Abstract
Schizophrenia is a mental illness affecting the normal lifestyle of adults and early adolescents incurring major symptoms as jumbled speech, involvement in everyday activities eventually got reduced, patients always struggle with attention and memory, reason being both the genetic and environmental factors responsible for altered brain chemistry and structure, resulting in schizophrenia and associated orphan diseases. The network biology describes the interactions among genes/proteins encoding molecular mechanisms of biological processes, development, and diseases. Besides, all the molecular networks, protein-protein Interaction Networks have been significant in distinguishing the pathogenesis of diseases and thereby drug discovery. The present meta-analysis prioritizes novel disease indications viz. rare and orphan diseases associated with target Glutamate Ionotropic Receptor NMDA Type Subunit 1, GRIN1 using text mining knowledge-based tools. Furthermore, ZINC database was virtually screened, and binding conformation of selected compounds was performed and resulted in the identification of Narciclasine (ZINC04097652) and Alvespimycin (ZINC73138787) as potential inhibitors. Furthermore, docked complexes were subjected to MD simulation studies which suggests that the identified leads could be a better potential drug to recuperate schizophrenia.
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Affiliation(s)
- Tulika Bhardwaj
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Irshad Ahmad
- Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Pallavi Somvanshi
- School of Computational & Integrative Sciences (SC&IS), Jawaharlal Nehru University, New Delhi, India.,Special Centre of Systems Medicine (SCSM), Jawaharlal Nehru University, New Delhi, India
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15
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Imam N, Bay S, Siddiqui MF, Yildirim O. Network Analysis Based Software Packages, Tools, and Web Servers to Accelerate Bioinformatics Research. BIOLOGICAL NETWORKS IN HUMAN HEALTH AND DISEASE 2023:51-64. [DOI: 10.1007/978-981-99-4242-8_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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16
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Alam A, Yildirim O, Siddiqui F, Imam N, Bay S. Network Medicine: Methods and Applications. BIOLOGICAL NETWORKS IN HUMAN HEALTH AND DISEASE 2023:75-90. [DOI: 10.1007/978-981-99-4242-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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17
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Jiang Y, Shi C, Tian S, Zhi F, Shen X, Shang D, Tian J. Comprehensive molecular characterization of hypertension-related genes in cancer. CARDIO-ONCOLOGY 2022; 8:10. [PMID: 35513851 PMCID: PMC9069779 DOI: 10.1186/s40959-022-00136-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/29/2022] [Indexed: 11/14/2022]
Abstract
Background During cancer treatment, patients have a significantly higher risk of developing cardiovascular complications such as hypertension. In this study, we investigated the internal relationships between hypertension and different types of cancer. Methods First, we comprehensively characterized the involvement of 10 hypertension-related genes across 33 types of cancer. The somatic copy number alteration (CNA) and single nucleotide variant (SNV) of each gene were identified for each type of cancer. Then, the expression patterns of hypertension-related genes were analyzed across 14 types of cancer. The hypertension-related genes were aberrantly expressed in different types of cancer, and some were associated with the overall survival of patients or the cancer stage. Subsequently, the interactions between hypertension-related genes and clinically actionable genes (CAGs) were identified by analyzing the co-expressions and protein–protein interactions. Results We found that certain hypertension-related genes were correlated with CAGs. Next, the pathways associated with hypertension-related genes were identified. The positively correlated pathways included epithelial to mesenchymal transition, hormone androgen receptor, and receptor tyrosine kinase, and the negatively correlated pathways included apoptosis, cell cycle, and DNA damage response. Finally, the correlations between hypertension-related genes and drug sensitivity were evaluated for different drugs and different types of cancer. The hypertension-related genes were all positively or negatively correlated with the resistance of cancer to the majority of anti-cancer drugs. These results highlight the importance of hypertension-related genes in cancer. Conclusions This study provides an approach to characterize the relationship between hypertension-related genes and cancers in the post-genomic era. Supplementary Information The online version contains supplementary material available at 10.1186/s40959-022-00136-z.
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18
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Xu J, Mao C, Hou Y, Luo Y, Binder JL, Zhou Y, Bekris LM, Shin J, Hu M, Wang F, Eng C, Oprea TI, Flanagan ME, Pieper AA, Cummings J, Leverenz JB, Cheng F. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell Rep 2022; 41:111717. [PMID: 36450252 PMCID: PMC9837836 DOI: 10.1016/j.celrep.2022.111717] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/01/2022] [Accepted: 11/02/2022] [Indexed: 12/03/2022] Open
Abstract
Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.
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Affiliation(s)
- Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jessica L Binder
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lynn M Bekris
- 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
| | - Jiyoung Shin
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, 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
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Margaret E Flanagan
- Department of Pathology and Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA; Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - James B Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, 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.
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19
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Pasquier C, Robichon A. Evolutionary Divergence of Phosphorylation to Regulate Interactive Protein Networks in Lower and Higher Species. Int J Mol Sci 2022; 23:ijms232214429. [PMID: 36430905 PMCID: PMC9697241 DOI: 10.3390/ijms232214429] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
The phosphorylation of proteins affects their functions in extensively documented circumstances. However, the role of phosphorylation in many interactive networks of proteins remains very elusive due to the experimental limits of exploring the transient interaction in a large complex of assembled proteins induced by stimulation. Previous studies have suggested that phosphorylation is a recent evolutionary process that differently regulates ortholog proteins in numerous lineages of living organisms to create new functions. Despite the fact that numerous phospho-proteins have been compared between species, little is known about the organization of the full phospho-proteome, the role of phosphorylation to orchestrate large interactive networks of proteins, and the intertwined phospho-landscape in these networks. In this report, we aimed to investigate the acquired role of phosphate addition in the phenomenon of protein networking in different orders of living organisms. Our data highlighted the acquired status of phosphorylation in organizing large, connected assemblages in Homo sapiens. The protein networking guided by phosphorylation turned out to be prominent in humans, chaotic in yeast, and weak in flies. Furthermore, the molecular functions of GO annotation enrichment regulated by phosphorylation were found to be drastically different between flies, yeast, and humans, suggesting an evolutionary drift specific to each species.
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Affiliation(s)
- Claude Pasquier
- I3S, Université Côte d’Azur, Campus SophiaTech, CNRS, 06903 Nice, France
- Correspondence:
| | - Alain Robichon
- INRAE, ISA, Université Côte d’Azur, Campus SophiaTech, CNRS, 06903 Nice, France
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20
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He B, Wang K, Xiang J, Bing P, Tang M, Tian G, Guo C, Xu M, Yang J. DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network. Brief Bioinform 2022; 23:6712302. [PMID: 36151744 DOI: 10.1093/bib/bbac405] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/01/2022] [Accepted: 08/21/2022] [Indexed: 12/14/2022] Open
Abstract
The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.
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Affiliation(s)
- Binsheng He
- Academician Workstation, Changsha Medical University, Changsha 410219, China.,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, P. R. China.,School of pharmacy, Changsha Medical University, Changsha 410219, P. R. China
| | - Kun Wang
- School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China
| | - Ju Xiang
- Academician Workstation, Changsha Medical University, Changsha 410219, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha 410219, China.,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, P. R. China.,School of pharmacy, Changsha Medical University, Changsha 410219, P. R. China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang 212001, Jiangsu, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing 100102, China
| | - Cheng Guo
- Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Miao Xu
- Broad institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Jialiang Yang
- Academician Workstation, Changsha Medical University, Changsha 410219, China.,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, P. R. China.,School of pharmacy, Changsha Medical University, Changsha 410219, P. R. China.,Geneis (Beijing) Co., Ltd., Beijing 100102, China
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21
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A systematic study of HIF1A cofactors in hypoxic cancer cells. Sci Rep 2022; 12:18962. [PMID: 36347941 PMCID: PMC9643333 DOI: 10.1038/s41598-022-23060-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022] Open
Abstract
Hypoxia inducible factor 1 alpha (HIF1A) is a transcription factor (TF) that forms highly structural and functional protein-protein interactions with other TFs to promote gene expression in hypoxic cancer cells. However, despite the importance of these TF-TF interactions, we still lack a comprehensive view of many of the TF cofactors involved and how they cooperate. In this study, we systematically studied HIF1A cofactors in eight cancer cell lines using the computational motif mining tool, SIOMICS, and discovered 201 potential HIF1A cofactors, which included 21 of the 29 known HIF1A cofactors in public databases. These 201 cofactors were statistically and biologically significant, with 19 of the top 37 cofactors in our study directly validated in the literature. The remaining 18 were novel cofactors. These discovered cofactors can be essential to HIF1A's regulatory functions and may lead to the discovery of new therapeutic targets in cancer treatment.
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22
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Shen WK, Chen SY, Gan ZQ, Zhang YZ, Yue T, Chen MM, Xue Y, Hu H, Guo AY. AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations. Nucleic Acids Res 2022; 51:D39-D45. [PMID: 36268869 PMCID: PMC9825474 DOI: 10.1093/nar/gkac907] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/30/2022] [Accepted: 10/05/2022] [Indexed: 01/29/2023] Open
Abstract
Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.
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Affiliation(s)
| | | | - Zi-Quan Gan
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yu-Zhu Zhang
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Tao Yue
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Miao-Miao Chen
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yu Xue
- Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Hui Hu
- Correspondence may also be addressed to Hui Hu.
| | - An-Yuan Guo
- To whom correspondence should be addressed. Tel: +86 27 8779 3177; Fax: +86 27 8779 3177;
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23
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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24
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Zuo Y, Hong Y, Zeng X, Zhang Q, Liu X. MLysPRED: graph-based multi-view clustering and multi-dimensional normal distribution resampling techniques to predict multiple lysine sites. Brief Bioinform 2022; 23:6661182. [PMID: 35953081 DOI: 10.1093/bib/bbac277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/11/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Posttranslational modification of lysine residues, K-PTM, is one of the most popular PTMs. Some lysine residues in proteins can be continuously or cascaded covalently modified, such as acetylation, crotonylation, methylation and succinylation modification. The covalent modification of lysine residues may have some special functions in basic research and drug development. Although many computational methods have been developed to predict lysine PTMs, up to now, the K-PTM prediction methods have been modeled and learned a single class of K-PTM modification. In view of this, this study aims to fill this gap by building a multi-label computational model that can be directly used to predict multiple K-PTMs in proteins. In this study, a multi-label prediction model, MLysPRED, is proposed to identify multiple lysine sites using features generated from human protein sequences. In MLysPRED, three kinds of multi-label sequence encoding algorithms (MLDBPB, MLPSDAAP, MLPSTAAP) are proposed and combined with three encoding strategies (CHHAA, DR and Kmer) to convert preprocessed lysine sequences into effective numerical features. A multidimensional normal distribution oversampling technique and graph-based multi-view clustering under-sampling algorithm were first proposed and incorporated to reduce the proportion of the original training samples, and multi-label nearest neighbor algorithm is used for classification. It is observed that MLysPRED achieved an Aiming of 92.21%, Coverage of 94.98%, Accuracy of 89.63%, Absolute-True of 81.46% and Absolute-False of 0.0682 on the independent datasets. Additionally, comparison of results with five existing predictors also indicated that MLysPRED is very promising and encouraging to predict multiple K-PTMs in proteins. For the convenience of the experimental scientists, 'MLysPRED' has been deployed as a user-friendly web-server at http://47.100.136.41:8181.
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Affiliation(s)
- Yun Zuo
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Yue Hong
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Hunan University, Changsha, China
| | - Qiang Zhang
- School of Computer Science and Technology, Dalian University of Technology (DLUT), China
| | - Xiangrong Liu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
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25
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Gupta C, Xu J, Jin T, Khullar S, Liu X, Alatkar S, Cheng F, Wang D. Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease. PLoS Comput Biol 2022; 18:e1010287. [PMID: 35849618 PMCID: PMC9333448 DOI: 10.1371/journal.pcbi.1010287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/28/2022] [Accepted: 06/07/2022] [Indexed: 12/03/2022] Open
Abstract
Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.
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Affiliation(s)
- Chirag Gupta
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Xiaoyu Liu
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Sayali Alatkar
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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26
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Hephzibah Cathryn R, Udhaya Kumar S, Younes S, Zayed H, George Priya Doss C. A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:85-164. [PMID: 35871897 DOI: 10.1016/bs.apcsb.2022.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
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Affiliation(s)
- R Hephzibah Cathryn
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - S Udhaya Kumar
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Salma Younes
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
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27
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Wang S, Wu R, Lu J, Jiang Y, Huang T, Cai YD. Protein-protein interaction networks as miners of biological discovery. Proteomics 2022; 22:e2100190. [PMID: 35567424 DOI: 10.1002/pmic.202100190] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/28/2022] [Accepted: 04/29/2022] [Indexed: 11/12/2022]
Abstract
Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Steven Wang
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Runxin Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jiaqi Lu
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
| | - Yijia Jiang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tao Huang
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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28
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Hou S, Zhang P, Yang K, Wang L, Ma C, Li Y, Li S. Decoding multilevel relationships with the human tissue-cell-molecule network. Brief Bioinform 2022; 23:6585388. [PMID: 35551347 DOI: 10.1093/bib/bbac170] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/09/2022] [Accepted: 04/16/2022] [Indexed: 02/01/2023] Open
Abstract
Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.
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Affiliation(s)
- Siyu Hou
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Kuo Yang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China.,School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Changzheng Ma
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Yanda Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, 100084 Beijing, China
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29
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Huang J, Wang D, Li H, Tang Y, Ma X, Tang H, Lin M, Liu Z. Antifungal activity of an artificial peptide aptamer SNP-D4 against Fusarium oxysporum. PeerJ 2022; 10:e12756. [PMID: 35223198 PMCID: PMC8877334 DOI: 10.7717/peerj.12756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/16/2021] [Indexed: 01/07/2023] Open
Abstract
Fusarium oxysporum f. sp. cubense (FOC4) is a pathogen of banana fusarium wilt, which is a serious problem that has plagued the tropical banana industry for many years. The pathogenic mechanism is complex and unclear, so the prevention and control in agricultural production applications is ineffective. SNP-D4, an artificial peptide aptamer, was identified and specifically inhibited FOC4. To evaluate the efficacy of SNP-D4, FoC4 spores were treated with purified SNP-D4 to calculate the germination and fungicide rates. Damage of FOC4 spores was observed by staining with propidium iodide (PI). Eight proteins of FOC4 were identified to have high affinity for SNP-D4 by a pull-down method combined with Q-Exactive mass spectrometry. Of these eight proteins, A0A5C6SPC6, the aldehyde dehydrogenase of FOC4, was selected as an example to scrutinize the interaction sites with SNP-D4. Molecular docking revealed that Thr66 on the peptide loop of SNP-D4 bound with Tyr437 near the catalytic center of A0A5C6SPC6. Subsequently 42 spore proteins which exhibited associations with the eight proteins were retrieved for protein-protein interaction analysis, demonstrating that SNP-D4 interfered with pathways including 'translation', 'folding, sorting and degradation', 'transcription', 'signal transduction' and 'cell growth and death', eventually causing the inhibition of growth of FOC4. This study not only investigated the possible pathogenic mechanism of FOC4, but also provided a potential antifungal agent SNP-D4 for use in the control of banana wilt disease.
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Affiliation(s)
- Junjun Huang
- College of Life Science Hainan University, Haikou, Hainan, China
| | - Dan Wang
- College of Life Science Hainan University, Haikou, Hainan, China
| | - Hong Li
- College of Life Science Hainan University, Haikou, Hainan, China
| | - Yanqiong Tang
- College of Life Science Hainan University, Haikou, Hainan, China
| | - Xiang Ma
- College of Life Science Hainan University, Haikou, Hainan, China
| | - Hongqian Tang
- College of Life Science Hainan University, Haikou, Hainan, China
| | - Min Lin
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhu Liu
- College of Life Science Hainan University, Haikou, Hainan, China
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
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32
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Ning W, Ma Y, Li S, Wang X, Pan H, Wei C, Zhang S, Bai D, Liu X, Deng Y, Acharya A, Pelekos G, Savkovic V, Li H, Gaus S, Haak R, Schmalz G, Ziebolz D, Ma Y, Xu Y. Shared Molecular Mechanisms between Atherosclerosis and Periodontitis by Analyzing the Transcriptomic Alterations of Peripheral Blood Monocytes. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:1498431. [PMID: 34899963 PMCID: PMC8664523 DOI: 10.1155/2021/1498431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/12/2021] [Indexed: 01/01/2023]
Abstract
OBJECTIVE This study investigated the nature of shared transcriptomic alterations in PBMs from periodontitis and atherosclerosis to unravel molecular mechanisms underpinning their association. METHODS Gene expression data from PBMs from patients with periodontitis and those with atherosclerosis were each downloaded from the GEO database. Differentially expressed genes (DEGs) in periodontitis and atherosclerosis were identified through differential gene expression analysis. The disease-related known genes related to periodontitis and atherosclerosis each were downloaded from the DisGeNET database. A Venn diagram was constructed to identify crosstalk genes from four categories: DEGs expressed in periodontitis, periodontitis-related known genes, DEGs expressed in atherosclerosis, and atherosclerosis-related known genes. A weighted gene coexpression network analysis (WGCNA) was performed to identify significant coexpression modules, and then, coexpressed gene interaction networks belonging to each significant module were constructed to identify the core crosstalk genes. RESULTS Functional enrichment analysis of significant modules obtained by WGCNA analysis showed that several pathways might play the critical crosstalk role in linking both diseases, including bacterial invasion of epithelial cells, platelet activation, and Mitogen-Activated Protein Kinases (MAPK) signaling. By constructing the gene interaction network of significant modules, the core crosstalk genes in each module were identified and included: for GSE23746 dataset, RASGRP2 in the blue module and VAMP7 and SNX3 in the green module, as well as HMGB1 and SUMO1 in the turquoise module were identified; for GSE61490 dataset, SEC61G, PSMB2, SELPLG, and FIBP in the turquoise module were identified. CONCLUSION Exploration of available transcriptomic datasets revealed core crosstalk genes (RASGRP2, VAMP7, SNX3, HMGB1, SUMO1, SEC61G, PSMB2, SELPLG, and FIBP) and significant pathways (bacterial invasion of epithelial cells, platelet activation, and MAPK signaling) as top candidate molecular linkage mechanisms between atherosclerosis and periodontitis.
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Affiliation(s)
- Wanchen Ning
- Stomatological Hospital, Southern Medical University, Guangzhou 510280, China
| | - Yihong Ma
- Department of Neurology, Graduate School of Medical Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Simin Li
- Stomatological Hospital, Southern Medical University, Guangzhou 510280, China
| | - Xin Wang
- Department of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Hongying Pan
- School of Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI 48109, USA
| | - Chenxuan Wei
- School of Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI 48109, USA
| | - Shaochuan Zhang
- Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Dongying Bai
- College of Animal Science and Technology, Henan University of Science and Technology, Luoyang 471003, China
| | - Xiangqiong Liu
- Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, 218 Anwaixiaoguanbeili Street, Chaoyang, Beijing 100029, China
| | - Yupei Deng
- Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, 218 Anwaixiaoguanbeili Street, Chaoyang, Beijing 100029, China
| | - Aneesha Acharya
- Dr D Y Patil Dental College and Hospital, Dr D Y Patil Vidyapeeth, Pimpri, Pune, India
| | - George Pelekos
- Faculty of Dentistry, University of Hong Kong, Hong KongChina
| | - Vuk Savkovic
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Liebigstr. 12, 04103 Leipzig, Germany
| | - Hanluo Li
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Liebigstr. 12, 04103 Leipzig, Germany
| | - Sebastian Gaus
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Liebigstr. 12, 04103 Leipzig, Germany
| | - Rainer Haak
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, 04103 Leipzig, Germany
| | - Gerhard Schmalz
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, 04103 Leipzig, Germany
| | - Dirk Ziebolz
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, 04103 Leipzig, Germany
| | - Yanbo Ma
- College of Animal Science and Technology, Henan University of Science and Technology, Luoyang 471003, China
| | - Yuzhen Xu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province 271000, China
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Wu J, Zhao M, Li T, Sun J, Chen Q, Yin C, Jia Z, Zhao C, Lin G, Ni Y, Xie G, Shi J, He K. HFIP: an integrated multi-omics data and knowledge platform for the precision medicine of heart failure. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6427587. [PMID: 34791105 PMCID: PMC8607296 DOI: 10.1093/database/baab076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 10/14/2021] [Accepted: 11/09/2021] [Indexed: 12/11/2022]
Abstract
As the terminal clinical phenotype of almost all types of cardiovascular diseases, heart
failure (HF) is a complex and heterogeneous syndrome leading to considerable morbidity and
mortality. Existing HF-related omics studies mainly focus on case/control comparisons,
small cohorts of special subtypes, etc., and a large amount of multi-omics data and
knowledge have been generated. However, it is difficult for researchers to obtain
biological and clinical insights from these scattered data and knowledge. In this paper,
we built the Heart Failure Integrated Platform (HFIP) for data exploration, fusion
analysis and visualization by collecting and curating existing multi-omics data and
knowledge from various public sources and also provided an auto-updating mechanism for
future integration. The developed HFIP contained 253 datasets (7842 samples), multiple
analysis flow, and 14 independent tools. In addition, based on the integration of existing
databases and literature, a knowledge base for HF was constructed with a scoring system
for evaluating the relationship between molecular signals and HF. The knowledge base
includes 1956 genes and annotation information. The literature mining module was developed
to assist the researcher to overview the hotspots and contexts in basic and clinical
research. HFIP can be used as a data-driven and knowledge-guided platform for the basic
and clinical research of HF. Database URL: http://heartfailure.medical-bigdata.com
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Affiliation(s)
- Jing Wu
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Min Zhao
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Tao Li
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Jinxiu Sun
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Qi Chen
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Chengliang Yin
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Zhilong Jia
- Research Center of Artificial Intelligence, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Chenghui Zhao
- Research Center of Biomedical Engineering, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Gui Lin
- Ping An Healthcare Technology, 316-1 Laoshan Road, Beijing 200120, China
| | - Yuan Ni
- Ping An Healthcare Technology, 316-1 Laoshan Road, Beijing 200120, China
| | - Guotong Xie
- Ping An Healthcare Technology, 316-1 Laoshan Road, Beijing 200120, China.,Ping An Healthcare and Technology Co, Ltd, 316-1 Laoshan Road, Shanghai 200120, China.,Ping An International Smart City Technology Co, Ltd, 5033 Yitian Road, Shenzhen 518046, China
| | - Jinlong Shi
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Kunlun He
- Research Center of Medical Big Data, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
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Zhang W, Tan X, Lin S, Gou Y, Han C, Zhang C, Ning W, Wang C, Xue Y. CPLM 4.0: an updated database with rich annotations for protein lysine modifications. Nucleic Acids Res 2021; 50:D451-D459. [PMID: 34581824 PMCID: PMC8728254 DOI: 10.1093/nar/gkab849] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 08/31/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
Here, we reported the compendium of protein lysine modifications (CPLM 4.0, http://cplm.biocuckoo.cn/), a data resource for various post-translational modifications (PTMs) specifically occurred at the side-chain amino group of lysine residues in proteins. From the literature and public databases, we collected 450 378 protein lysine modification (PLM) events, and combined them with the existing data of our previously developed protein lysine modification database (PLMD 3.0). In total, CPLM 4.0 contained 592 606 experimentally identified modification events on 463 156 unique lysine residues of 105 673 proteins for up to 29 types of PLMs across 219 species. Furthermore, we carefully annotated the data using the knowledge from 102 additional resources that covered 13 aspects, including variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, DNA & RNA element, protein structure, chemical-target relation, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation, and physicochemical property. Compared to PLMD 3.0 and other existing resources, CPLM 4.0 achieved a >2-fold increase in collection of PLM events, with a data volume of ∼45GB. We anticipate that CPLM 4.0 can serve as a more useful database for further study of PLMs.
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Affiliation(s)
- Weizhi Zhang
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaodan Tan
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Shaofeng Lin
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yujie Gou
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Cheng Han
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Chi Zhang
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Wanshan Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Chenwei Wang
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yu Xue
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.,Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing, Jiangsu 210031, China
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Li H, Ma X, Tang Y, Wang D, Zhang Z, Liu Z. Network-based analysis of virulence factors for uncovering Aeromonas veronii pathogenesis. BMC Microbiol 2021; 21:188. [PMID: 34162325 PMCID: PMC8223281 DOI: 10.1186/s12866-021-02261-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/15/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Aeromonas veronii is a bacterial pathogen in aquaculture, which produces virulence factors to enable it colonize and evade host immune defense. Given that experimental verification of virulence factors is time-consuming and laborious, few virulence factors have been characterized. Moreover, most studies have only focused on single virulence factors, resulting in biased interpretation of the pathogenesis of A. veronii. RESULTS In this study, a PPI network at genome-wide scale for A. veronii was first constructed followed by prediction and mapping of virulence factors on the network. When topological characteristics were analyzed, the virulence factors had higher degree and betweenness centrality than other proteins in the network. In particular, the virulence factors tended to interact with each other and were enriched in two network modules. One of the modules mainly consisted of histidine kinases, response regulators, diguanylate cyclases and phosphodiesterases, which play important roles in two-component regulatory systems and the synthesis and degradation of cyclic-diGMP. Construction of the interspecies PPI network between A. veronii and its host Oreochromis niloticus revealed that the virulence factors interacted with homologous proteins in the host. Finally, the structures and interacting sites of the virulence factors during interaction with host proteins were predicted. CONCLUSIONS The findings here indicate that the virulence factors probably regulate the virulence of A. veronii by involving in signal transduction pathway and manipulate host biological processes by mimicking and binding competitively to host proteins. Our results give more insight into the pathogenesis of A. veronii and provides important information for designing targeted antibacterial drugs.
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Affiliation(s)
- Hong Li
- School of Life Sciences, Hainan University, Haikou, China
| | - Xiang Ma
- School of Life Sciences, Hainan University, Haikou, China
| | - Yanqiong Tang
- School of Life Sciences, Hainan University, Haikou, China
| | - Dan Wang
- Key Laboratory for Sustainable Utilization of Tropical Bioresource, College of Tropical Crops, Hainan University, Haikou, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Zhu Liu
- School of Life Sciences, Hainan University, Haikou, China.
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Huckstep H, Fearnley LG, Davis MJ. Measuring pathway database coverage of the phosphoproteome. PeerJ 2021; 9:e11298. [PMID: 34113485 PMCID: PMC8162239 DOI: 10.7717/peerj.11298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/29/2021] [Indexed: 12/02/2022] Open
Abstract
Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data.
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Affiliation(s)
- Hannah Huckstep
- Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Liam G. Fearnley
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia
- Division of Population Health, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Melissa J. Davis
- Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia
- Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
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Wu P, Xie X, Chen M, Sun J, Cai L, Wei J, Yang L, Huang X, Wang L. Elucidation of the Mechanisms and Molecular Targets of Qishen Yiqi Formula for the Treatment of Pulmonary Arterial Hypertension using a Bioinformatics/Network Topology-based Strategy. Comb Chem High Throughput Screen 2021; 24:701-715. [PMID: 33076804 DOI: 10.2174/1386207323666201019145354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/06/2020] [Accepted: 09/12/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND OBJECTIVE Qishen Yiqi formula (QSYQ) is used to treat cardiovascular disease in the clinical practice of traditional Chinese medicine. However, few studies have explored whether QSYQ affects pulmonary arterial hypertension (PAH), and the mechanisms of action and molecular targets of QSYQ for the treatment of PAH are unclear. A bioinformatics/network topology-based strategy was used to identify the bioactive ingredients, putative targets, and molecular mechanisms of QSYQ in PAH. METHODS A network pharmacology-based strategy was employed by integrating active component gathering, target prediction, PAH gene collection, network topology, and gene enrichment analysis to systematically explore the multicomponent synergistic mechanisms. RESULTS In total, 107 bioactive ingredients of QSYQ and 228 ingredient targets were identified. Moreover, 234 PAH-related differentially expressed genes with a |fold change| >2 and an adjusted P value < 0.005 were identified between the PAH patient and control groups, and 266 therapeutic targets were identified. The pathway enrichment analysis indicated that 85 pathways, including the PI3K-Akt, MAPK, and HIF-1 signaling pathways, were significantly enriched. TP53 was the core target gene, and 7 other top genes (MAPK1, RELA, NFKB1, CDKN1A, AKT1, MYC, and MDM2) were the key genes in the gene-pathway network based on the effects of QSYQ on PAH. CONCLUSION An integrative investigation based on network pharmacology may elucidate the multicomponent synergistic mechanisms of QSYQ in PAH and lay a foundation for further animal experiments, human clinical trials and rational clinical applications of QSYQ.
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Affiliation(s)
- Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiaona Xie
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Mayun Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Junwei Sun
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Luqiong Cai
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Jinqiu Wei
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Lin Yang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiaoying Huang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Liangxing Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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Xu Y, Li H, He X, Huang Y, Wang S, Wang L, Fu C, Ye H, Li X, Asakawa T. Identification of the Key Role of NF-κB Signaling Pathway in the Treatment of Osteoarthritis With Bushen Zhuangjin Decoction, a Verification Based on Network Pharmacology Approach. Front Pharmacol 2021; 12:637273. [PMID: 33912052 PMCID: PMC8072665 DOI: 10.3389/fphar.2021.637273] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/11/2021] [Indexed: 01/13/2023] Open
Abstract
This study aimed to identify whether the NF-κB signaling pathway plays a key role in the treatment of osteoarthritis (OA) with Bushen Zhuangjin Decoction (BZD) based on a typical network pharmacology approach (NPA). Four sequential experiments were performed: 1) conventional high-performance liquid chromatography (HPLC), 2) preliminary observation of the therapeutic effects of BZD, 3) NPA using three OA-related gene expression profiles, and 4) verification of the key pathway identified by NPA. Only one HPLC-verified compound (paeoniflorin) was identified from the candidate compounds discovered by NPA. The genes verified in the preliminary observation were also identified by NPA. NPA identified a key role for the NF-κB signaling pathway in the treatment of OA with BZD, which was confirmed by conventional western blot analysis. This study identified and verified NF-κB signaling pathway as the most important inflammatory signaling pathway involved in the mechanisms of BZD for treating OA by comparing the NPA results with conventional methods. Our findings also indicate that NPA is a powerful tool for exploring the molecular targets of complex herbal formulations, such as BZD.
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Affiliation(s)
- Yunteng Xu
- College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Hui Li
- Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China.,College of Pharmacy Science, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Xiaojuan He
- Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China.,College of Pharmacy Science, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Yanfeng Huang
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China
| | - Shengjie Wang
- Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China.,College of Pharmacy Science, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Lili Wang
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China
| | - Changlong Fu
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China
| | - Hongzhi Ye
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China
| | - Xihai Li
- College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Tetsuya Asakawa
- Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Department of Neurosurgery, Hamamatsu University School of Medicine, Hamamatsu-city, Japan.,Department of Neurology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
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He L, Liu L, Li T, Zhuang D, Dai J, Wang B, Bi L. Exploring the Imbalance of Periodontitis Immune System From the Cellular to Molecular Level. Front Genet 2021; 12:653209. [PMID: 33841510 PMCID: PMC8033214 DOI: 10.3389/fgene.2021.653209] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 03/08/2021] [Indexed: 01/22/2023] Open
Abstract
Periodontitis is a common chronic inflammatory disease of periodontal tissue, mostly concentrated in people over 30 years old. Statistics show that compared with foreign countries, the prevalence of periodontitis in China is as high as 40%, and the prevalence of periodontal disease is more than 90%, which must arouse our great attention. Diagnosis and treatment of periodontitis currently rely mainly on clinical criteria, and the exploration of the etiologic criteria is relatively lacking. We, therefore, have explored the pathogenesis of periodontitis from the perspective of immune imbalance. By predicting the fraction of 22 immune cells in periodontitis tissues and comparing them with normal tissues, we found that multiple immune cell infiltration in periodontitis tissues was inhibited and this feature can clearly distinguish periodontitis from normal tissues. Further, protein interaction network (PPI) and transcription regulation network have been constructed based on differentially expressed genes (DEGs) to explore the interaction function modules and regulation pathways. Three functional modules have been revealed and top TFs such as EGR1 and ETS1 have been shown to regulate the expression of periodontitis-related immune genes that play an important role in the formation of the immunosuppressive microenvironment. The classifier was also used to verify the reliability of periodontitis features obtained at the cellular and molecular levels. In conclusion, we have revealed the immune microenvironment and molecular characteristics of periodontitis, which will help to better understand the mechanism of periodontitis and its application in clinical diagnosis and treatment.
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Affiliation(s)
- Longfei He
- Department of Stomatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,Department of Stomatology, Weifang People's Hospital, Weifang, China
| | - Lijuan Liu
- Department of Stomatology, Weifang People's Hospital, Weifang, China
| | - Ti Li
- Department of Stomatology, Weifang People's Hospital, Weifang, China
| | - Deshu Zhuang
- Department of Stomatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC, Canada
| | - Jiayin Dai
- Department of Stomatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Bo Wang
- Department of Stomatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liangjia Bi
- Department of Stomatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
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Sengupta A, Naresh G, Mishra A, Parashar D, Narad P. Proteome analysis using machine learning approaches and its applications to diseases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:161-216. [PMID: 34340767 DOI: 10.1016/bs.apcsb.2021.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.
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Affiliation(s)
- Abhishek Sengupta
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - G Naresh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Astha Mishra
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Diksha Parashar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Priyanka Narad
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India.
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41
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Tiwari R, Mishra AR, Gupta A, Nayak D. Structural similarity-based prediction of host factors associated with SARS-CoV-2 infection and pathogenesis. J Biomol Struct Dyn 2021; 40:5868-5879. [PMID: 33506741 PMCID: PMC7852281 DOI: 10.1080/07391102.2021.1874532] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The current pandemic resulted from SARS-CoV-2 still remains as the major public health concern globally. The precise mechanism of viral pathogenesis is not fully understood, which remains a major hurdle for medical intervention. Here we generated an interactome profile of protein-protein interactions based on host and viral protein structural similarities information. Further computational biological study combined with Gene enrichment analysis predicted key enriched pathways associated with viral pathogenesis. The results show that axon guidance, membrane trafficking, vesicle-mediated transport, apoptosis, clathrin-mediated endocytosis, Vpu mediated degradation of CD4 T cell, and interferon-gamma signaling are key events associated in SARS-CoV-2 life cycle. Further, degree centrality analysis reveals that IRF1/9/7, TP53, and CASP3, UBA52, and UBC are vital proteins for IFN-γ-mediated signaling, apoptosis, and proteasomal degradation of CD4, respectively. We crafted chronological events of the virus life cycle. The SARS-CoV-2 enters through clathrin-mediated endocytosis, and the genome is trafficked to the early endosomes in a RAB5-dependent manner. It is predicted to replicate in a double-membrane vesicle (DMV) composed of the endoplasmic reticulum, autophagosome, and ERAD machinery. The SARS-CoV-2 down-regulates host translational machinery by interacting with protein kinase R, PKR-like endoplasmic reticulum kinase, and heme-regulated inhibitor and can phosphorylate eIF2a. The virion assembly occurs in the ER-Golgi intermediate compartment (ERGIC) organized by the spike and matrix protein. Collectively, we have established a spatial link between viral entry, RNA synthesis, assembly, pathogenesis, and their associated diverse host factors, those could pave the way for therapeutic intervention.
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Affiliation(s)
- Ritudhwaj Tiwari
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Anurag R Mishra
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Advika Gupta
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Debasis Nayak
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
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Meng X, Li J, Zhang Q, Chen F, Bian C, Yao X, Yan J, Xu Z, Risacher SL, Saykin AJ, Liang H, Shen L. Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease. BMC Genomics 2020; 21:896. [PMID: 33372590 PMCID: PMC7771059 DOI: 10.1186/s12864-020-07282-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 11/25/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Jin Li
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, 132012, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Chenyuan Bian
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
| | - Zhe Xu
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, 150001, China.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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43
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Identifying patient-specific flow of signal transduction perturbed by multiple single-nucleotide alterations. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0227-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Li Y, Qiu S, Zhong W, Li Y, Liu Y, Cheng Y, Liu Y. Association Between DCC Polymorphisms and Susceptibility to Autism Spectrum Disorder. J Autism Dev Disord 2020; 50:3800-3809. [PMID: 32144606 DOI: 10.1007/s10803-020-04417-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Autism spectrum disorder (ASD) represents a group of childhood-onset lifelong neuro-developmental disorders. However, the association between single nucleotide polymorphisms (SNPs) in the deleted in colorectal carcinoma (DCC) gene and ASD susceptibility remains unclear. We investigated the association between ASD susceptibility and seven SNPs in DCC on the basis of a case-control study (231 ASD cases and 242 controls) in Chinese Han. We found that there was no association between ASD susceptibility and the seven SNPs in DCC; however, T-A haplotype (rs2229082-rs2270954), T-A-T-C haplotype (rs2229082-rs2270954-rs2292043-rs2292044), C-G-T-C-T haplotype (rs934345-rs17753970-rs2229082-rs2270954-rs2292043), C-G-T-C-T-G haplotype (rs934345-rs17753970-rs2229082-rs2270954-rs2292043-rs2292044), and G-G-T-C-C-C-C haplotype (rs934345-rs17753970-rs2229082-rs2270954-rs2292043-rs2292044-rs16956878) were associated with ASD susceptibility. Our results indicate that the haplotypes formed on the basis of the seven SNPs in DCC may be implicated in ASD.
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Affiliation(s)
- Yan Li
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, 130021, China
| | - Shuang Qiu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, 130021, China
| | - Weijing Zhong
- Chunguang Rehabilitation Hospital, Changchun, 130021, China
| | - Yong Li
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, 130021, China
| | - Yunkai Liu
- Institute of Translational Medicine, The First Hospital of Jilin University, Changchun, 130021, China
| | - Yi Cheng
- Institute of Translational Medicine, The First Hospital of Jilin University, Changchun, 130021, China.
| | - Yawen Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, 130021, China.
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Carvalho AS, Moraes MCS, Hyun Na C, Fierro-Monti I, Henriques A, Zahedi S, Bodo C, Tranfield EM, Sousa AL, Farinho A, Rodrigues LV, Pinto P, Bárbara C, Mota L, de Abreu TT, Semedo J, Seixas S, Kumar P, Costa-Silva B, Pandey A, Matthiesen R. Is the Proteome of Bronchoalveolar Lavage Extracellular Vesicles a Marker of Advanced Lung Cancer? Cancers (Basel) 2020; 12:cancers12113450. [PMID: 33233545 PMCID: PMC7699733 DOI: 10.3390/cancers12113450] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/11/2020] [Accepted: 11/19/2020] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Bronchoalveolar lavage is routinely collected during bronchoscopy for cytology analysis in the diagnostic of lung cancer. Due to low sensitivity of this method, early-stage cancers are undetected, lowering the treatment success. In this study, we analyzed extracellular vesicles isolated from bronchoalveolar lavage of lung cancer suspects by mass spectrometry-based proteomics. The protein composition of bronchoalveolar lavage extracellular vesicles of late-stage cancer showed a higher proteome complexity associated with mortality within the two year follow-up period. We identified a potential therapeutic target DNMT3B complex which was significantly expressed in bronchoalveolar lavage extracellular vesicles as well as in tumor tissue. Bronchoalveolar lavage extracellular vesicles proteome analysis of immune markers indicates the presence of markers of innate immune and fibroblast cells. Abstract Acellular bronchoalveolar lavage (BAL) proteomics can partially separate lung cancer from non-lung cancer patients based on principal component analysis and multivariate analysis. Furthermore, the variance in the proteomics data sets is correlated mainly with lung cancer status and, to a lesser extent, smoking status and gender. Despite these advances BAL small and large extracellular vehicles (EVs) proteomes reveal aberrant protein expression in paracrine signaling mechanisms in cancer initiation and progression. We consequently present a case-control study of 24 bronchoalveolar lavage extracellular vesicle samples which were analyzed by state-of-the-art liquid chromatography-mass spectrometry (LC-MS). We obtained evidence that BAL EVs proteome complexity correlated with lung cancer stage 4 and mortality within two years´ follow-up (p value = 0.006). The potential therapeutic target DNMT3B complex is significantly up-regulated in tumor tissue and BAL EVs. The computational analysis of the immune and fibroblast cell markers in EVs suggests that patients who deceased within the follow-up period display higher marker expression indicative of innate immune and fibroblast cells (four out of five cases). This study provides insights into the proteome content of BAL EVs and their correlation to clinical outcomes.
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Affiliation(s)
- Ana Sofia Carvalho
- Computational and Experimental Biology Group, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciencias Medicas, Universidade NOVA de Lisboa, Campo dos Martires da Patria, 130, 1169-056 Lisboa, Portugal; (I.F.-M.); (A.H.); (S.Z.)
- Correspondence: (A.S.C.); (R.M.)
| | - Maria Carolina Strano Moraes
- Systems Oncology Group, Champalimaud Research, Champalimaud Centre for the Unknown, Av. Brasilia, Doca de Pedroucos, 1400-038 Lisbon, Portugal; (M.C.S.M.); (C.B.); (B.C.-S.)
| | - Chan Hyun Na
- Department of Neurology, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| | - Ivo Fierro-Monti
- Computational and Experimental Biology Group, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciencias Medicas, Universidade NOVA de Lisboa, Campo dos Martires da Patria, 130, 1169-056 Lisboa, Portugal; (I.F.-M.); (A.H.); (S.Z.)
| | - Andreia Henriques
- Computational and Experimental Biology Group, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciencias Medicas, Universidade NOVA de Lisboa, Campo dos Martires da Patria, 130, 1169-056 Lisboa, Portugal; (I.F.-M.); (A.H.); (S.Z.)
| | - Sara Zahedi
- Computational and Experimental Biology Group, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciencias Medicas, Universidade NOVA de Lisboa, Campo dos Martires da Patria, 130, 1169-056 Lisboa, Portugal; (I.F.-M.); (A.H.); (S.Z.)
| | - Cristian Bodo
- Systems Oncology Group, Champalimaud Research, Champalimaud Centre for the Unknown, Av. Brasilia, Doca de Pedroucos, 1400-038 Lisbon, Portugal; (M.C.S.M.); (C.B.); (B.C.-S.)
| | - Erin M Tranfield
- Electron Microscopy Facility, Instituto Gulbenkian de Ciência—Rua da Quinta Grande, 6, 2780-156 Oeiras, Portugal; (E.M.T.); (A.L.S.)
| | - Ana Laura Sousa
- Electron Microscopy Facility, Instituto Gulbenkian de Ciência—Rua da Quinta Grande, 6, 2780-156 Oeiras, Portugal; (E.M.T.); (A.L.S.)
| | - Ana Farinho
- iNOVA4Health—Advancing Precision Medicine, CEDOC—Chronic Diseases Research Centre, NOVA Medical School/Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Martires da Patria, 130, 1169-056 Lisboa, Portugal;
| | - Luís Vaz Rodrigues
- Department of Pneumology, Unidade Local de Saúde da Guarda (USLGuarda), 6300-659 Guarda, Portugal;
| | - Paula Pinto
- Unidade de Técnicas Invasivas Pneumológicas, Pneumologia II, Hospital Pulido Valente, Centro Hospitalar Lisboa Norte, 1649-028 Lisbon, Portugal; (P.P.); (L.M.); (T.T.d.A.); (J.S.)
| | - Cristina Bárbara
- Instituto de Saúde Ambiental (ISAMB), Faculdade de Medicina, Universidade de Lisboa, Centro Hospitalar Universitário Lisboa Norte, 1649-028 Lisbon, Portugal;
| | - Leonor Mota
- Unidade de Técnicas Invasivas Pneumológicas, Pneumologia II, Hospital Pulido Valente, Centro Hospitalar Lisboa Norte, 1649-028 Lisbon, Portugal; (P.P.); (L.M.); (T.T.d.A.); (J.S.)
| | - Tiago Tavares de Abreu
- Unidade de Técnicas Invasivas Pneumológicas, Pneumologia II, Hospital Pulido Valente, Centro Hospitalar Lisboa Norte, 1649-028 Lisbon, Portugal; (P.P.); (L.M.); (T.T.d.A.); (J.S.)
| | - Júlio Semedo
- Unidade de Técnicas Invasivas Pneumológicas, Pneumologia II, Hospital Pulido Valente, Centro Hospitalar Lisboa Norte, 1649-028 Lisbon, Portugal; (P.P.); (L.M.); (T.T.d.A.); (J.S.)
| | - Susana Seixas
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, 4200-135 Porto, Portugal;
| | - Prashant Kumar
- Institute of Bioinformatics, Discoverer building, ITPL, Bangalore 560066, India; (P.K.); (A.P.)
- Manipal Academy of Higher Education (MAHE), Manipal 576104, India
| | - Bruno Costa-Silva
- Systems Oncology Group, Champalimaud Research, Champalimaud Centre for the Unknown, Av. Brasilia, Doca de Pedroucos, 1400-038 Lisbon, Portugal; (M.C.S.M.); (C.B.); (B.C.-S.)
| | - Akhilesh Pandey
- Institute of Bioinformatics, Discoverer building, ITPL, Bangalore 560066, India; (P.K.); (A.P.)
- Manipal Academy of Higher Education (MAHE), Manipal 576104, India
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Rune Matthiesen
- Computational and Experimental Biology Group, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciencias Medicas, Universidade NOVA de Lisboa, Campo dos Martires da Patria, 130, 1169-056 Lisboa, Portugal; (I.F.-M.); (A.H.); (S.Z.)
- Correspondence: (A.S.C.); (R.M.)
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Tiwari R, Mishra AR, Mikaeloff F, Gupta S, Mirazimi A, Byrareddy SN, Neogi U, Nayak D. In silico and in vitro studies reveal complement system drives coagulation cascade in SARS-CoV-2 pathogenesis. Comput Struct Biotechnol J 2020; 18:3734-3744. [PMID: 33200027 PMCID: PMC7657020 DOI: 10.1016/j.csbj.2020.11.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/04/2020] [Accepted: 11/04/2020] [Indexed: 01/08/2023] Open
Abstract
The emergence and continued spread of SARS-CoV-2 have resulted in a public health emergency across the globe. The lack of knowledge on the precise mechanism of viral pathogenesis is impeding medical intervention. In this study, we have taken both in silico and in vitro experimental approaches to unravel the mechanism of viral pathogenesis associated with complement and coagulation pathways. Based on the structural similarities of viral and host proteins, we initially generated a protein-protein interactome profile. Further computational analysis combined with Gene Ontology (GO) analysis and KEGG pathway analysis predicted key annotated pathways associated with viral pathogenesis. These include MAPK signaling, complement, and coagulation cascades, endocytosis, PD-L1 expression, PD-1 checkpoint pathway in cancer and C-type lectin receptor signaling pathways. Degree centrality analysis pinned down to MAPK1, MAPK3, AKT1, and SRC are crucial drivers of signaling pathways and often overlap with the associated pathways. Most strikingly, the complement and coagulation cascade and platelet activation pathways are interconnected, presumably directing thrombotic activity observed in severe or critical cases of COVID-19. This is complemented by in vitro studies of Huh7 cell infection and analysis of the transcriptome and proteomic profile of gene candidates during viral infection. The most known candidates associated with complement and coagulation cascade signaling by KEGG pathway analysis showed significant up-regulated fold change during viral infection. Collectively both in silico and in vitro studies suggest complement and coagulation cascade signaling are a mechanism for intravascular coagulation, thrombotic changes, and associated complications in severe COVID-19 patients.
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Affiliation(s)
- Ritudhwaj Tiwari
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, MP, India
| | - Anurag R. Mishra
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, MP, India
| | - Flora Mikaeloff
- Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Soham Gupta
- Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ali Mirazimi
- Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- Public Health Agency of Sweden, Solna, Sweden
- National Veterinary Institute, Uppsala, Sweden
| | - Siddappa N. Byrareddy
- Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA
| | - Ujjwal Neogi
- Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Molecular Microbiology and Immunology and the Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA
| | - Debasis Nayak
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, MP, India
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Khatun MS, Shoombuatong W, Hasan MM, Kurata H. Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction. Curr Genomics 2020; 21:454-463. [PMID: 33093807 PMCID: PMC7536797 DOI: 10.2174/1389202921999200625103936] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/19/2020] [Accepted: 05/27/2020] [Indexed: 12/22/2022] Open
Abstract
Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.
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Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
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Randhawa V, Pathania S. Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge. Brief Funct Genomics 2020; 19:364-376. [PMID: 32678894 DOI: 10.1093/bfgp/elaa015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/31/2020] [Accepted: 06/15/2020] [Indexed: 01/17/2023] Open
Abstract
Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.
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Affiliation(s)
- Vinay Randhawa
- Department of Biochemistry, Panjab University, Chandigarh, 160014, India
| | - Shivalika Pathania
- Department of Biotechnology, Panjab University, Chandigarh, 160014, India
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Guo Y, Ning W, Jiang P, Lin S, Wang C, Tan X, Yao L, Peng D, Xue Y. GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains. Cells 2020; 9:cells9051266. [PMID: 32443803 PMCID: PMC7290655 DOI: 10.3390/cells9051266] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/17/2020] [Accepted: 05/18/2020] [Indexed: 12/13/2022] Open
Abstract
Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling into downstream pathways. Although massive phosphorylation sites (p-sites) have been reported, most of their interacting PPBDs are unknown. Here, we collected 4458 known PPBD-specific binding p-sites (PBSs), considerably improved our previously developed group-based prediction system (GPS) algorithm, and implemented a deep learning plus transfer learning strategy for model training. Then, we developed a new online service named GPS-PBS, which can hierarchically predict PBSs of 122 single PPBD clusters belonging to two groups and 16 families. By comparison, GPS-PBS achieved a highly competitive accuracy against other existing tools. Using GPS-PBS, we predicted 371,018 mammalian p-sites that potentially interact with at least one PPBD, and revealed that various PPBD-containing proteins (PPCPs) and protein kinases (PKs) can simultaneously regulate the same p-sites to orchestrate important pathways, such as the PI3K-Akt signaling pathway. Taken together, we anticipate GPS-PBS can be a great help for further dissecting phosphorylation signaling networks.
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Affiliation(s)
- Yaping Guo
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Wanshan Ning
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Peiran Jiang
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Shaofeng Lin
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Chenwei Wang
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaodan Tan
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Lan Yao
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Di Peng
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yu Xue
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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50
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Huang KY, Lee TY, Kao HJ, Ma CT, Lee CC, Lin TH, Chang WC, Huang HD. dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications. Nucleic Acids Res 2020; 47:D298-D308. [PMID: 30418626 PMCID: PMC6323979 DOI: 10.1093/nar/gky1074] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 10/19/2018] [Indexed: 12/25/2022] Open
Abstract
The dbPTM (http://dbPTM.mbc.nctu.edu.tw/) has been maintained for over 10 years with the aim to provide functional and structural analyses for post-translational modifications (PTMs). In this update, dbPTM not only integrates more experimentally validated PTMs from available databases and through manual curation of literature but also provides PTM-disease associations based on non-synonymous single nucleotide polymorphisms (nsSNPs). The high-throughput deep sequencing technology has led to a surge in the data generated through analysis of association between SNPs and diseases, both in terms of growth amount and scope. This update thus integrated disease-associated nsSNPs from dbSNP based on genome-wide association studies. The PTM substrate sites located at a specified distance in terms of the amino acids encoded from nsSNPs were deemed to have an association with the involved diseases. In recent years, increasing evidence for crosstalk between PTMs has been reported. Although mass spectrometry-based proteomics has substantially improved our knowledge about substrate site specificity of single PTMs, the fact that the crosstalk of combinatorial PTMs may act in concert with the regulation of protein function and activity is neglected. Because of the relatively limited information about concurrent frequency and functional relevance of PTM crosstalk, in this update, the PTM sites neighboring other PTM sites in a specified window length were subjected to motif discovery and functional enrichment analysis. This update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research.
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Affiliation(s)
- Kai-Yao Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Chen-Tse Ma
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Chao-Chun Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Tsai-Hsuan Lin
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Wen-Chi Chang
- Institute of Tropical Plant Sciences, College of Biosciences and Biotechnology, National Cheng Kung University, Tainan 70101, Taiwan
| | - Hsien-Da Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
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