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Kliche J, Simonetti L, Krystkowiak I, Kuss H, Diallo M, Rask E, Nilsson J, Davey NE, Ivarsson Y. Proteome-scale characterisation of motif-based interactome rewiring by disease mutations. Mol Syst Biol 2024:10.1038/s44320-024-00055-4. [PMID: 39009827 DOI: 10.1038/s44320-024-00055-4] [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: 09/15/2023] [Revised: 06/14/2024] [Accepted: 06/28/2024] [Indexed: 07/17/2024] Open
Abstract
Whole genome and exome sequencing are reporting on hundreds of thousands of missense mutations. Taking a pan-disease approach, we explored how mutations in intrinsically disordered regions (IDRs) break or generate protein interactions mediated by short linear motifs. We created a peptide-phage display library tiling ~57,000 peptides from the IDRs of the human proteome overlapping 12,301 single nucleotide variants associated with diverse phenotypes including cancer, metabolic diseases and neurological diseases. By screening 80 human proteins, we identified 366 mutation-modulated interactions, with half of the mutations diminishing binding, and half enhancing binding or creating novel interaction interfaces. The effects of the mutations were confirmed by affinity measurements. In cellular assays, the effects of motif-disruptive mutations were validated, including loss of a nuclear localisation signal in the cell division control protein CDC45 by a mutation associated with Meier-Gorlin syndrome. The study provides insights into how disease-associated mutations may perturb and rewire the motif-based interactome.
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Affiliation(s)
- Johanna Kliche
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Leandro Simonetti
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Izabella Krystkowiak
- Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, SW3 6JB, Chelsea, London, UK
| | - Hanna Kuss
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, DE-48149, Münster, Germany
| | - Marcel Diallo
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Emma Rask
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Jakob Nilsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Norman E Davey
- Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, SW3 6JB, Chelsea, London, UK.
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden.
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2
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Csikász-Nagy A, Fichó E, Noto S, Reguly I. Computational tools to predict context-specific protein complexes. Curr Opin Struct Biol 2024; 88:102883. [PMID: 38986166 DOI: 10.1016/j.sbi.2024.102883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/21/2024] [Accepted: 06/19/2024] [Indexed: 07/12/2024]
Abstract
Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.
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Affiliation(s)
- Attila Csikász-Nagy
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | | | - Santiago Noto
- Cytocast Hungary Kft, Budapest, Hungary; Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil
| | - István Reguly
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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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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Nussinov R, Yavuz BR, Demirel HC, Arici MK, Jang H, Tuncbag N. Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide. Front Cell Dev Biol 2024; 12:1376639. [PMID: 39015651 PMCID: PMC11249571 DOI: 10.3389/fcell.2024.1376639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | | | - M. Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | - Nurcan Tuncbag
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Türkiye
- School of Medicine, Koc University, Istanbul, Türkiye
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
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5
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Fonódi M, Nagy L, Boratkó A. Role of Protein Phosphatases in Tumor Angiogenesis: Assessing PP1, PP2A, PP2B and PTPs Activity. Int J Mol Sci 2024; 25:6868. [PMID: 38999976 PMCID: PMC11241275 DOI: 10.3390/ijms25136868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/14/2024] Open
Abstract
Tumor angiogenesis, the formation of new blood vessels to support tumor growth and metastasis, is a complex process regulated by a multitude of signaling pathways. Dysregulation of signaling pathways involving protein kinases has been extensively studied, but the role of protein phosphatases in angiogenesis within the tumor microenvironment remains less explored. However, among angiogenic pathways, protein phosphatases play critical roles in modulating signaling cascades. This review provides a comprehensive overview of the involvement of protein phosphatases in tumor angiogenesis, highlighting their diverse functions and mechanisms of action. Protein phosphatases are key regulators of cellular signaling pathways by catalyzing the dephosphorylation of proteins, thereby modulating their activity and function. This review aims to assess the activity of the protein tyrosine phosphatases and serine/threonine phosphatases. These phosphatases exert their effects on angiogenic signaling pathways through various mechanisms, including direct dephosphorylation of angiogenic receptors and downstream signaling molecules. Moreover, protein phosphatases also crosstalk with other signaling pathways involved in angiogenesis, further emphasizing their significance in regulating tumor vascularization, including endothelial cell survival, sprouting, and vessel maturation. In conclusion, this review underscores the pivotal role of protein phosphatases in tumor angiogenesis and accentuate their potential as therapeutic targets for anti-angiogenic therapy in cancer.
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Affiliation(s)
| | | | - Anita Boratkó
- Department of Medical Chemistry, Faculty of Medicine, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary; (M.F.); (L.N.)
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Gualdi F, Oliva B, Piñero J. Genopyc: a Python library for investigating the functional effects of genomic variants associated to complex diseases. Bioinformatics 2024; 40:btae379. [PMID: 38889282 PMCID: PMC11211212 DOI: 10.1093/bioinformatics/btae379] [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: 11/29/2023] [Revised: 05/21/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Integrative Biomedicl Informatics, Research Program on Biomedical Informatics (IBI - GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF) C/ del Dr. Aiguader 88 Barcelona 08003 Spain.Understanding the genetic basis of complex diseases is one of the main challenges in modern genomics. However, current tools often lack the versatility to efficiently analyze the intricate relationships between genetic variations and disease outcomes. To address this, we introduce Genopyc, a novel Python library designed for comprehensive investigation of how the variants associated to complex diseases affects downstream pathways. Genopyc offers an extensive suite of functions for heterogeneous data mining and visualization, enabling researchers to delve into and integrate biological information from large-scale genomic datasets. RESULTS In this work, we present the Genopyc library through application to real-world genome wide association studies variants. Using Genopyc to investigate the functional consequences of variants associated to intervertebral disc degeneration enabled a deeper understanding of the potential dysregulated pathways involved in the disease, which can be explored and visualized by exploiting the functionalities featured in the package. Genopyc emerges as a powerful asset for researchers, facilitating the investigation of complex diseases paving the way for more targeted therapeutic interventions. AVAILABILITY AND IMPLEMENTATION Genopyc is available on pip https://pypi.org/project/genopyc/.The source code of Genopyc is available at https://github.com/freh-g/genopyc. A tutorial notebook is available at https://github.com/freh-g/genopyc/blob/main/tutorials/Genopyc_tutorial_notebook.ipynb. Finally, a detailed documentation is available at: https://genopyc.readthedocs.io/en/latest/.
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Affiliation(s)
- Francesco Gualdi
- Integrative Biomedical Informatics, Research Program on Biomedical Informatics (IBI-GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), C/ del Dr. Aiguader 88, Barcelona 08003, Spain
- Structural Bioinformatics Lab, Research Program on Biomedical Informatics (SBI-GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), C/ del Dr. Aiguader 88, Barcelona 08003, Spain
| | - Baldomero Oliva
- Structural Bioinformatics Lab, Research Program on Biomedical Informatics (SBI-GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), C/ del Dr. Aiguader 88, Barcelona 08003, Spain
| | - Janet Piñero
- Integrative Biomedical Informatics, Research Program on Biomedical Informatics (IBI-GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), C/ del Dr. Aiguader 88, Barcelona 08003, Spain
- Medbioinformatics Solutions SL, Barcelona, C/ rambla Cataluña 14, Barcelona 08007, Spain
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Bonsor M, Ammar O, Schnoegl S, Wanker EE, Silva Ramos E. Polyglutamine disease proteins: Commonalities and differences in interaction profiles and pathological effects. Proteomics 2024; 24:e2300114. [PMID: 38615323 DOI: 10.1002/pmic.202300114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Currently, nine polyglutamine (polyQ) expansion diseases are known. They include spinocerebellar ataxias (SCA1, 2, 3, 6, 7, 17), spinal and bulbar muscular atrophy (SBMA), dentatorubral-pallidoluysian atrophy (DRPLA), and Huntington's disease (HD). At the root of these neurodegenerative diseases are trinucleotide repeat mutations in coding regions of different genes, which lead to the production of proteins with elongated polyQ tracts. While the causative proteins differ in structure and molecular mass, the expanded polyQ domains drive pathogenesis in all these diseases. PolyQ tracts mediate the association of proteins leading to the formation of protein complexes involved in gene expression regulation, RNA processing, membrane trafficking, and signal transduction. In this review, we discuss commonalities and differences among the nine polyQ proteins focusing on their structure and function as well as the pathological features of the respective diseases. We present insights from AlphaFold-predicted structural models and discuss the biological roles of polyQ-containing proteins. Lastly, we explore reported protein-protein interaction networks to highlight shared protein interactions and their potential relevance in disease development.
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Affiliation(s)
- Megan Bonsor
- Department of Neuroproteomics, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Orchid Ammar
- Department of Neuroproteomics, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Sigrid Schnoegl
- Department of Neuroproteomics, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Erich E Wanker
- Department of Neuroproteomics, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Eduardo Silva Ramos
- Department of Neuroproteomics, Max Delbrück Center for Molecular Medicine, Berlin, Germany
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Dehghan Z, Mirmotalebisohi SA, Mozafar M, Sameni M, Saberi F, Derakhshanfar A, Moaedi J, Zohrevand H, Zali H. Deciphering the similarities and disparities of molecular mechanisms behind respiratory epithelium response to HCoV-229E and SARS-CoV-2 and drug repurposing, a systems biology approach. Daru 2024; 32:215-235. [PMID: 38652363 PMCID: PMC11087451 DOI: 10.1007/s40199-024-00507-0] [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: 09/17/2022] [Accepted: 02/08/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Identifying the molecular mechanisms behind SARS-CoV-2 disparities and similarities will help find new treatments. The present study determines networks' shared and non-shared (specific) crucial elements in response to HCoV-229E and SARS-CoV-2 viruses to recommend candidate medications. METHODS We retrieved the omics data on respiratory cells infected with HCoV-229E and SARS-CoV-2, constructed PPIN and GRN, and detected clusters and motifs. Using a drug-gene interaction network, we determined the similarities and disparities of mechanisms behind their host response and drug-repurposed. RESULTS CXCL1, KLHL21, SMAD3, HIF1A, and STAT1 were the shared DEGs between both viruses' protein-protein interaction network (PPIN) and gene regulatory network (GRN). The NPM1 was a specific critical node for HCoV-229E and was a Hub-Bottleneck shared between PPI and GRN in HCoV-229E. The HLA-F, ADCY5, TRIM14, RPF1, and FGA were the seed proteins in subnetworks of the SARS-CoV-2 PPI network, and HSPA1A and RPL26 proteins were the seed in subnetworks of the PPI network of HCOV-229E. TRIM14, STAT2, and HLA-F played the same role for SARS-CoV-2. Top enriched KEGG pathways included cell cycle and proteasome in HCoV-229E and RIG-I-like receptor, Chemokine, Cytokine-cytokine, NOD-like receptor, and TNF signaling pathways in SARS-CoV-2. We suggest some candidate medications for COVID-19 patient lungs, including Noscapine, Isoetharine mesylate, Cycloserine, Ethamsylate, Cetylpyridinium, Tretinoin, Ixazomib, Vorinostat, Venetoclax, Vorinostat, Ixazomib, Venetoclax, and epoetin alfa for further in-vitro and in-vivo investigations. CONCLUSION We suggested CXCL1, KLHL21, SMAD3, HIF1A, and STAT1, ADCY5, TRIM14, RPF1, and FGA, STAT2, and HLA-F as critical genes and Cetylpyridinium, Cycloserine, Noscapine, Ethamsylate, Epoetin alfa, Isoetharine mesylate, Ribavirin, and Tretinoin drugs to study further their importance in treating COVID-19 lung complications.
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Affiliation(s)
- Zeinab Dehghan
- Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Seyed Amir Mirmotalebisohi
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Mozafar
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Marzieh Sameni
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Saberi
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amin Derakhshanfar
- Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran.
- Center of Comparative and Experimental Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Javad Moaedi
- Center of Comparative and Experimental Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hassan Zohrevand
- Student Research Committee, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hakimeh Zali
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Jalali P, Yaghoobi A, Rezaee M, Zabihi MR, Piroozkhah M, Aliyari S, Salehi Z. Decoding common genetic alterations between Barrett's esophagus and esophageal adenocarcinoma: A bioinformatics analysis. Heliyon 2024; 10:e31194. [PMID: 38803922 PMCID: PMC11128929 DOI: 10.1016/j.heliyon.2024.e31194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Background Esophageal adenocarcinoma (EAC) is a common cancer with a poor prognosis in advanced stages. Therefore, early EAC diagnosis and treatment have gained attention in recent decades. It has been found that various pathological changes, particularly Barrett's Esophagus (BE), can occur in the esophageal tissue before the development of EAC. In this study, we aimed to identify the molecular contributor in BE to EAC progression by detecting the essential regulatory genes that are differentially expressed in both BE and EAC. Materials and methods We conducted a comprehensive bioinformatics analysis to detect BE and EAC-associated genes. The common differentially expressed genes (DEGs) and common single nucleotide polymorphisms (SNPs) were detected using the GEO and DisGeNET databases, respectively. Then, hub genes and the top modules within the protein-protein interaction network were identified. Moreover, the co-expression network of the top module by the HIPPIE database was constructed. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. Lastly, we inspected the DGIdb database for possible interacted drugs. Results Our microarray dataset analysis identified 92 common DEGs between BE and EAC with significant enrichment in skin and epidermis development genes. The study also identified 22 common SNPs between BE and EAC. The top module of PPI network analysis included SCEL, KRT6A, SPRR1A, SPRR1B, SPRR3, PPL, SPRR2B, EVPL, and CSTA. We constructed a ceRNA network involving three specific mRNAs, 23 miRNAs, and 101 selected circRNAs. According to the results from the DGIdb database, TD101 was found to interact with the KRT6A gene. Conclusion The present study provides novel potential candidate genes that may be involved in the molecular association between Esophageal adenocarcinoma and Barrett's Esophagus, resulting in developing the diagnostic tools and therapeutic targets to prevent progression of BE to EAC.
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Affiliation(s)
- Pooya Jalali
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Yaghoobi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Malihe Rezaee
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Zabihi
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Moein Piroozkhah
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shahram Aliyari
- Division of Applied Bioinformatics, German Cancer Research Center DKFZ Heidelberg, Iran
| | - Zahra Salehi
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Research Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran
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10
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Liu X, Abad L, Chatterjee L, Cristea IM, Varjosalo M. Mapping protein-protein interactions by mass spectrometry. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38742660 DOI: 10.1002/mas.21887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.
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Affiliation(s)
- Xiaonan Liu
- Department of Physiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Lawrence Abad
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Lopamudra Chatterjee
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Markku Varjosalo
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
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11
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Tutanov O, Shefer A, Shefer E, Ruzankin P, Tsentalovich Y, Tamkovich S. DNA-Binding Proteins and Passenger Proteins in Plasma DNA-Protein Complexes: Imprint of Parental Cells or Key Mediators of Carcinogenesis Processes? Int J Mol Sci 2024; 25:5165. [PMID: 38791202 PMCID: PMC11121045 DOI: 10.3390/ijms25105165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/03/2024] [Accepted: 05/04/2024] [Indexed: 05/26/2024] Open
Abstract
Knowledge of the composition of proteins that interact with plasma DNA will provide a better understanding of the homeostasis of circulating nucleic acids and the various modes of interaction with target cells, which may be useful in the development of gene targeted therapy approaches. The goal of the present study is to shed light on the composition and architecture of histone-containing nucleoprotein complexes (NPCs) from the blood plasma of healthy females (HFs) and breast cancer patients (BCPs) and to explore the relationship of proteins with crucial steps of tumor progression: epithelial-mesenchymal transition (EMT), cell proliferation, invasion, cell migration, stimulation of angiogenesis, and immune response. MALDI-TOF mass spectrometric analysis of NPCs isolated from blood samples using affine chromatography was performed. Bioinformatics analysis showed that the shares of DNA-binding proteins in the compositions of NPCs in normal and cancer patients are comparable and amount to 40% and 33%, respectively; in total, we identified 38 types of DNA-binding motifs. Functional enrichment analysis using FunRich 3.13 showed that, in BCP blood, the share of DNA-binding proteins involved in nucleic acid metabolism increased, while the proportion of proteins involved in intercellular communication and signal transduction decreased. The representation of NPC passenger proteins in breast cancer also changes: the proportion of proteins involved in transport increases and the share of proteins involved in energy biological pathways decreases. Moreover, in the HF blood, proteins involved in the processes of apoptosis were more represented in the composition of NPCs and in the BCP blood-in the processes of active secretion. For the first time, bioinformatics approaches were used to visualize the architecture of circulating NPCs in the blood and to show that breast cancer has an increased representation of passenger proteins involved in EMT, cell proliferation, invasion, cell migration, and immune response. Using breast cancer protein data from the Human Protein Atlas (HPA) and DEPC, we found that 86% of NPC proteins in the blood of BCPs were not previously annotated in these databases. The obtained data may indirectly indicate directed protein sorting in NPCs, which, along with extracellular vesicles, can not only be diagnostically significant molecules for liquid biopsy, but can also carry out the directed transfer of genetic material from donor cells to recipient cells.
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Affiliation(s)
- Oleg Tutanov
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, USA;
| | - Aleksei Shefer
- Laboratory of Molecular Medicine, Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia;
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Evgenii Shefer
- Novosibirsk State University, 630090 Novosibirsk, Russia
- Laboratory of Applied Inverse Problems, Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Pavel Ruzankin
- Novosibirsk State University, 630090 Novosibirsk, Russia
- Laboratory of Applied Inverse Problems, Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Yuri Tsentalovich
- Laboratory of Proteomics and Metabolomics, International Tomography Center, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia;
| | - Svetlana Tamkovich
- Laboratory of Molecular Medicine, Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia;
- Novosibirsk State University, 630090 Novosibirsk, Russia
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12
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Mouillet-Richard S, Gougelet A, Passet B, Brochard C, Le Corre D, Pitasi CL, Joubel C, Sroussi M, Gallois C, Lavergne J, Castille J, Vilotte M, Daniel-Carlier N, Pilati C, de Reyniès A, Djouadi F, Colnot S, André T, Taieb J, Vilotte JL, Romagnolo B, Laurent-Puig P. Wnt, glucocorticoid and cellular prion protein cooperate to drive a mesenchymal phenotype with poor prognosis in colon cancer. J Transl Med 2024; 22:337. [PMID: 38589873 PMCID: PMC11003154 DOI: 10.1186/s12967-024-05164-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/04/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND The mesenchymal subtype of colorectal cancer (CRC), associated with poor prognosis, is characterized by abundant expression of the cellular prion protein PrPC, which represents a candidate therapeutic target. How PrPC is induced in CRC remains elusive. This study aims to elucidate the signaling pathways governing PrPC expression and to shed light on the gene regulatory networks linked to PrPC. METHODS We performed in silico analyses on diverse datasets of in vitro, ex vivo and in vivo models of mouse CRC and patient cohorts. We mined ChIPseq studies and performed promoter analysis. CRC cell lines were manipulated through genetic and pharmacological approaches. We created mice combining conditional inactivation of Apc in intestinal epithelial cells and overexpression of the human prion protein gene PRNP. Bio-informatic analyses were carried out in two randomized control trials totalizing over 3000 CRC patients. RESULTS In silico analyses combined with cell-based assays identified the Wnt-β-catenin and glucocorticoid pathways as upstream regulators of PRNP expression, with subtle differences between mouse and human. We uncover multiple feedback loops between PrPC and these two pathways, which translate into an aggravation of CRC pathogenesis in mouse. In stage III CRC patients, the signature defined by PRNP-CTNNB1-NR3C1, encoding PrPC, β-catenin and the glucocorticoid receptor respectively, is overrepresented in the poor-prognosis, mesenchymal subtype and associates with reduced time to recurrence. CONCLUSIONS An unleashed PrPC-dependent vicious circle is pathognomonic of poor prognosis, mesenchymal CRC. Patients from this aggressive subtype of CRC may benefit from therapies targeting the PRNP-CTNNB1-NR3C1 axis.
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Affiliation(s)
- Sophie Mouillet-Richard
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France.
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France.
| | - Angélique Gougelet
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
| | - Bruno Passet
- University of Paris-Saclay, INRAE, AgroParisTech, UMR1313 GABI, 78350, Jouy-en-Josas, France
| | - Camille Brochard
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Institut du Cancer Paris CARPEM, APHP, Department of Pathology, APHP.Centre-Université Paris Cité, Hôpital Européen G. Pompidou, Paris, France
| | - Delphine Le Corre
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Caterina Luana Pitasi
- Université Paris Cité, Institut Cochin, Inserm, CNRS, F-75014, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Camille Joubel
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Marine Sroussi
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Claire Gallois
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
- Institut du Cancer Paris CARPEM, APHP, Hepatogastroenterology and GI Oncology Department, APHP.Centre-Université Paris Cité, Hôpital Européen G. Pompidou, Paris, France
| | - Julien Lavergne
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Histology, Imaging and Cytometry Center (CHIC), Paris, France
| | - Johan Castille
- University of Paris-Saclay, INRAE, AgroParisTech, UMR1313 GABI, 78350, Jouy-en-Josas, France
| | - Marthe Vilotte
- University of Paris-Saclay, INRAE, AgroParisTech, UMR1313 GABI, 78350, Jouy-en-Josas, France
| | - Nathalie Daniel-Carlier
- University of Paris-Saclay, INRAE, AgroParisTech, UMR1313 GABI, 78350, Jouy-en-Josas, France
| | - Camilla Pilati
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Aurélien de Reyniès
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Fatima Djouadi
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Sabine Colnot
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Thierry André
- Saint-Antoine Hospital, INSERM, Unité Mixte de Recherche Scientifique 938, Sorbonne Université, Paris, France
| | - Julien Taieb
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
- Institut du Cancer Paris CARPEM, APHP, Hepatogastroenterology and GI Oncology Department, APHP.Centre-Université Paris Cité, Hôpital Européen G. Pompidou, Paris, France
| | - Jean-Luc Vilotte
- University of Paris-Saclay, INRAE, AgroParisTech, UMR1313 GABI, 78350, Jouy-en-Josas, France
| | - Béatrice Romagnolo
- Université Paris Cité, Institut Cochin, Inserm, CNRS, F-75014, Paris, France
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France
| | - Pierre Laurent-Puig
- Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France.
- Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France.
- Institut du Cancer Paris CARPEM, APHP, Department of Biology, APHP.Centre-Université Paris Cité, Hôpital Européen G. Pompidou, Paris, France.
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13
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Inge MM, Miller R, Hook H, Bray D, Keenan JL, Zhao R, Gilmore TD, Siggers T. Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.05.588333. [PMID: 38617258 PMCID: PMC11014505 DOI: 10.1101/2024.04.05.588333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced H3K27ac. Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data supports clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.
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Affiliation(s)
- MM Inge
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- These authors contributed equally
| | - R Miller
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- These authors contributed equally
| | - H Hook
- Department of Biology, Boston University, Boston, MA, USA
| | - D Bray
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - JL Keenan
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - R Zhao
- Department of Biology, Boston University, Boston, MA, USA
| | - TD Gilmore
- Department of Biology, Boston University, Boston, MA, USA
| | - T Siggers
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
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14
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Deritei D, Inuzuka H, Castaldi PJ, Yun JH, Xu Z, Anamika WJ, Asara JM, Guo F, Zhou X, Glass K, Wei W, Silverman EK. HHIP protein interactions in lung cells provide insight into COPD pathogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.01.586839. [PMID: 38617310 PMCID: PMC11014494 DOI: 10.1101/2024.04.01.586839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP , a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP , its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2 , and FBLN5 . These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.
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15
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Sun J, Qu J, Zhao C, Zhang X, Liu X, Wang J, Wei C, Liu X, Wang M, Zeng P, Tang X, Ling X, Qing L, Jiang S, Chen J, Chen TSR, Kuang Y, Gao J, Zeng X, Huang D, Yuan Y, Fan L, Yu H, Ding J. Precise prediction of phase-separation key residues by machine learning. Nat Commun 2024; 15:2662. [PMID: 38531854 DOI: 10.1038/s41467-024-46901-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.
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Affiliation(s)
- Jun Sun
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiale Qu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Cai Zhao
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyao Zhang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyu Liu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jia Wang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, 511436, China
| | - Chao Wei
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyi Liu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Mulan Wang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Pengguihang Zeng
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiuxiao Tang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaoru Ling
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Li Qing
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shaoshuai Jiang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiahao Chen
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tara S R Chen
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China
| | - Yalan Kuang
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Jinhang Gao
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Dongfeng Huang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China
| | - Yong Yuan
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
| | - Lili Fan
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Jinan University, Guangzhou, Guangdong, China.
| | - Haopeng Yu
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
| | - Junjun Ding
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China.
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16
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Liu Y, Li X, Chen C, Ding N, Zheng P, Chen X, Ma S, Yang M. TCMNPAS: a comprehensive analysis platform integrating network formulaology and network pharmacology for exploring traditional Chinese medicine. Chin Med 2024; 19:50. [PMID: 38519956 PMCID: PMC10958928 DOI: 10.1186/s13020-024-00924-y] [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: 01/12/2024] [Accepted: 03/11/2024] [Indexed: 03/25/2024] Open
Abstract
The application of network formulaology and network pharmacology has significantly advanced the scientific understanding of traditional Chinese medicine (TCM) treatment mechanisms in disease. The field of herbal biology is experiencing a surge in data generation. However, researchers are encountering challenges due to the fragmented nature of the data and the reliance on programming tools for data analysis. We have developed TCMNPAS, a comprehensive analysis platform that integrates network formularology and network pharmacology. This platform is designed to investigate in-depth the compatibility characteristics of TCM formulas and their potential molecular mechanisms. TCMNPAS incorporates multiple resources and offers a range of functions designed for automated analysis implementation, including prescription mining, molecular docking, network pharmacology analysis, and visualization. These functions enable researchers to analyze and obtain core herbs and core formulas from herbal prescription data through prescription mining. Additionally, TCMNPAS facilitates virtual screening of active compounds in TCM and its formulas through batch molecular docking, allowing for the rapid construction and analysis of networks associated with "herb-compound-target-pathway" and disease targets. Built upon the integrated analysis concept of network formulaology and network pharmacology, TCMNPAS enables quick point-and-click completion of network-based association analysis, spanning from core formula mining from clinical data to the exploration of therapeutic targets for disease treatment. TCMNPAS serves as a powerful platform for uncovering the combinatorial rules and mechanism of TCM formulas holistically. We distribute TCMNPAS within an open-source R package at GitHub ( https://github.com/yangpluszhu/tcmnpas ), and the project is freely available at http://54.223.75.62:3838/ .
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Affiliation(s)
- Yishu Liu
- LongHua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Xue Li
- LongHua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Chao Chen
- LongHua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Nan Ding
- LongHua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Peiyong Zheng
- LongHua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Xiaoyun Chen
- LongHua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Shiyu Ma
- Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
| | - Ming Yang
- LongHua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
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17
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Mac Donagh J, Marchesini A, Spiga A, Fallico MJ, Arrías PN, Monzon AM, Vagiona AC, Gonçalves-Kulik M, Mier P, Andrade-Navarro MA. Structured Tandem Repeats in Protein Interactions. Int J Mol Sci 2024; 25:2994. [PMID: 38474241 DOI: 10.3390/ijms25052994] [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: 02/09/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
Tandem repeats (TRs) in protein sequences are consecutive, highly similar sequence motifs. Some types of TRs fold into structural units that pack together in ensembles, forming either an (open) elongated domain or a (closed) propeller, where the last unit of the ensemble packs against the first one. Here, we examine TR proteins (TRPs) to see how their sequence, structure, and evolutionary properties favor them for a function as mediators of protein interactions. Our observations suggest that TRPs bind other proteins using large, structured surfaces like globular domains; in particular, open-structured TR ensembles are favored by flexible termini and the possibility to tightly coil against their targets. While, intuitively, open ensembles of TRs seem prone to evolve due to their potential to accommodate insertions and deletions of units, these evolutionary events are unexpectedly rare, suggesting that they are advantageous for the emergence of the ancestral sequence but are early fixed. We hypothesize that their flexibility makes it easier for further proteins to adapt to interact with them, which would explain their large number of protein interactions. We provide insight into the properties of open TR ensembles, which make them scaffolds for alternative protein complexes to organize genes, RNA and proteins.
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Affiliation(s)
- Juan Mac Donagh
- Science and Technology Department, National University of Quilmes, Bernal B1876, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires C1033AAJ, Argentina
| | - Abril Marchesini
- National Scientific and Technical Research Council (CONICET), Buenos Aires C1033AAJ, Argentina
- Biotechnology and Molecular Biology Institute (IBBM, UNLP-CONICET), Faculty of Exact Sciences, University of La Plata, La Plata 1900, Argentina
| | - Agostina Spiga
- Science and Technology Department, National University of Quilmes, Bernal B1876, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires C1033AAJ, Argentina
| | - Maximiliano José Fallico
- Laboratory of Bioactive Compound Research and Development, Faculty of Exact Sciences, University of La Plata, La Plata 1900, Argentina
| | - Paula Nazarena Arrías
- Department of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
| | - Alexander Miguel Monzon
- Department of Information Engineering, University of Padova, Via Giovanni Gradenigo 6/B, 35131 Padova, Italy
| | - Aimilia-Christina Vagiona
- Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Mariane Gonçalves-Kulik
- Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Pablo Mier
- Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Miguel A Andrade-Navarro
- Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
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18
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Idrees S, Paudel KR. Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions. J Cell Commun Signal 2024; 18:e12014. [PMID: 38545252 PMCID: PMC10964934 DOI: 10.1002/ccs3.12014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 12/11/2023] [Indexed: 06/29/2024] Open
Abstract
Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular SciencesUniversity of New South WalesSydneyNew South WalesAustralia
- Centre for InflammationCentenary Institute and the University of Technology SydneySchool of Life SciencesFaculty of ScienceSydneyNew South WalesAustralia
| | - Keshav Raj Paudel
- Centre for InflammationCentenary Institute and the University of Technology SydneySchool of Life SciencesFaculty of ScienceSydneyNew South WalesAustralia
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19
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Gomez SM, Axtman AD, Willson TM, Major MB, Townsend RR, Sorger PK, Johnson GL. Illuminating function of the understudied druggable kinome. Drug Discov Today 2024; 29:103881. [PMID: 38218213 DOI: 10.1016/j.drudis.2024.103881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/21/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024]
Abstract
The human kinome, with more than 500 proteins, is crucial for cell signaling and disease. Yet, about one-third of kinases lack in-depth study. The Data and Resource Generating Center for Understudied Kinases has developed multiple resources to address this challenge including creation of a heavy amino acid peptide library for parallel reaction monitoring and quantitation of protein kinase expression, use of understudied kinases tagged with a miniTurbo-biotin ligase to determine interaction networks by proximity-dependent protein biotinylation, NanoBRET probe development for screening chemical tool target specificity in live cells, characterization of small molecule chemical tools inhibiting understudied kinases, and computational tools for defining kinome architecture. These resources are available through the Dark Kinase Knowledgebase, supporting further research into these understudied protein kinases.
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Affiliation(s)
- Shawn M Gomez
- University of North Carolina School of Medicine, Chapel Hill, NC, USA.
| | - Alison D Axtman
- University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Timothy M Willson
- University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Michael B Major
- Washington University School of Medicine in St. Louis, MO, USA
| | - Reid R Townsend
- Washington University School of Medicine in St. Louis, MO, USA
| | | | - Gary L Johnson
- University of North Carolina School of Medicine, Chapel Hill, NC, USA.
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20
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Karunakaran KB, Jain S, Brahmachari SK, Balakrishnan N, Ganapathiraju MK. Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:26. [PMID: 38413605 PMCID: PMC10899210 DOI: 10.1038/s41537-024-00439-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/24/2024] [Indexed: 02/29/2024]
Abstract
Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.
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Affiliation(s)
- Kalyani B Karunakaran
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India.
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan.
| | - Sanjeev Jain
- National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, India.
| | | | - N Balakrishnan
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India
| | - Madhavi K Ganapathiraju
- Department of Computer Science, Carnegie Mellon University Qatar, Doha, Qatar.
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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21
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Shao L, Zhao Y, Heinrich M, Prieto-Garcia JM, Manzoni C. Active natural compounds perturb the melanoma risk-gene network. G3 (BETHESDA, MD.) 2024; 14:jkad274. [PMID: 38035793 PMCID: PMC10849364 DOI: 10.1093/g3journal/jkad274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/27/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023]
Abstract
Cutaneous melanoma is an aggressive type of skin cancer with a complex genetic landscape caused by the malignant transformation of melanocytes. This study aimed at providing an in silico network model based on the systematic profiling of the melanoma-associated genes considering germline mutations, somatic mutations, and genome-wide association study signals accounting for a total of 232 unique melanoma risk genes. A protein-protein interaction network was constructed using the melanoma risk genes as seeds and evaluated to describe the functional landscape in which the melanoma genes operate within the cellular milieu. Not only were the majority of the melanoma risk genes able to interact with each other at the protein level within the core of the network, but this showed significant enrichment for genes whose expression is altered in human melanoma specimens. Functional annotation showed the melanoma risk network to be significantly associated with processes related to DNA metabolism and telomeres, DNA damage and repair, cellular ageing, and response to radiation. We further explored whether the melanoma risk network could be used as an in silico tool to predict the efficacy of anti-melanoma phytochemicals, that are considered active molecules with potentially less systemic toxicity than classical cytotoxic drugs. A significant portion of the melanoma risk network showed differential expression when SK-MEL-28 human melanoma cells were exposed to the phytochemicals harmine and berberine chloride. This reinforced our hypothesis that the network modeling approach not only provides an alternative way to identify molecular pathways relevant to disease but it may also represent an alternative screening approach to prioritize potentially active compounds.
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Affiliation(s)
- Luying Shao
- Department of Pharmaceutical and Biological Chemistry, UCL School of Pharmacy, WC1N 1AX London, UK
| | - Yibo Zhao
- Department of Pharmacology, UCL School of Pharmacy, WC1N 1AX London, UK
| | - Michael Heinrich
- Department of Pharmaceutical and Biological Chemistry, UCL School of Pharmacy, WC1N 1AX London, UK
- Chinese Medicine Research Center, and Department of Pharmaceutical Sciences and Chinese Medicine Resources, College of Chinese Medicine, China Medical University, Taichung City 404333, Taiwan
| | - Jose M Prieto-Garcia
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, L3 3AF Liverpool, UK
| | - Claudia Manzoni
- Department of Pharmacology, UCL School of Pharmacy, WC1N 1AX London, UK
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22
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Zeylan M, Senyuz S, Picón-Pagès P, García-Elías A, Tajes M, Muñoz FJ, Oliva B, Garcia-Ojalvo J, Barbu E, Vicente R, Nattel S, Ois A, Puig-Pijoan A, Keskin O, Gursoy A. Shared Proteins and Pathways of Cardiovascular and Cognitive Diseases: Relation to Vascular Cognitive Impairment. J Proteome Res 2024; 23:560-573. [PMID: 38252700 PMCID: PMC10846560 DOI: 10.1021/acs.jproteome.3c00289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/29/2023] [Accepted: 12/06/2023] [Indexed: 01/24/2024]
Abstract
One of the primary goals of systems medicine is the detection of putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular cognitive impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular cognitive genes and proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.
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Affiliation(s)
- Melisa
E. Zeylan
- Computational
Sciences and Engineering, Graduate School of Science and Engineering, Koç University, Istanbul 34450, Türkiye
| | - Simge Senyuz
- Computational
Sciences and Engineering, Graduate School of Science and Engineering, Koç University, Istanbul 34450, Türkiye
| | - Pol Picón-Pagès
- Laboratory
of Molecular Physiology, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Anna García-Elías
- Laboratory
of Molecular Physiology, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Marta Tajes
- Laboratory
of Molecular Physiology, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Francisco J. Muñoz
- Laboratory
of Molecular Physiology, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Baldomero Oliva
- Laboratory
of Structural Bioinformatics (GRIB), Department of Medicine and Life
Sciences, Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Jordi Garcia-Ojalvo
- Laboratory
of Dynamical Systems Biology, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Eduard Barbu
- Institute
of Computer Science, University of Tartu, Tartu, 50090, Estonia
| | - Raul Vicente
- Institute
of Computer Science, University of Tartu, Tartu, 50090, Estonia
| | - Stanley Nattel
- Department
of Medicine and Research Center, Montreal Heart Institute and Université
de Montréal; Institute of Pharmacology, West German Heart and
Vascular Center, University Duisburg-Essen,
Germany; IHU LIRYC and Fondation Bordeaux Université, Bordeaux 33000, France
| | - Angel Ois
- Department
of Neurology, Hospital Del Mar. Hospital
Del Mar - Medical Research Institute and Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Albert Puig-Pijoan
- Department
of Neurology, Hospital Del Mar. Hospital
Del Mar - Medical Research Institute and Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Ozlem Keskin
- Department
of Chemical and Biological Engineering, Koç University, Istanbul 34450, Türkiye
| | - Attila Gursoy
- Department
of Computer Engineering, Koç University, Istanbul 34450, Türkiye
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23
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Tasneem A, Sultan A, Singh P, Bairagya HR, Almasoudi HH, Alhazmi AYM, Binshaya AS, Hakami MA, Alotaibi BS, Abdulaziz Eisa A, Alolaiqy ASI, Hasan MR, Dev K, Dohare R. Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins. Front Genet 2024; 15:1292280. [PMID: 38370514 PMCID: PMC10869566 DOI: 10.3389/fgene.2024.1292280] [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: 09/11/2023] [Accepted: 01/08/2024] [Indexed: 02/20/2024] Open
Abstract
Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host-virus protein-protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection. Objective: This study aims to identify therapeutic target proteins in humans that could act as virus-host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors. Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 ("hCoV-2"), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient's mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein. Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein. Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.
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Affiliation(s)
- Alvea Tasneem
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Armiya Sultan
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Prithvi Singh
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Hridoy R. Bairagya
- Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Haringhata, West Bengal, India
| | - Hassan Hussain Almasoudi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| | | | - Abdulkarim S. Binshaya
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Mohammed Ageeli Hakami
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al- Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Bader S. Alotaibi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al- Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Alaa Abdulaziz Eisa
- Department of Medical Laboratory Technology, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | | | - Mohammad Raghibul Hasan
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al- Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Kapil Dev
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Ravins Dohare
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
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24
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Nascimento-Gonçalves E, Seixas F, Palmeira C, Martins G, Fonseca C, Duarte JA, Faustino-Rocha AI, Colaço B, Pires MJ, Neuparth MJ, Moreira-Gonçalves D, Fardilha M, Henriques MC, Patrício D, Pelech S, Ferreira R, Oliveira PA. Lifelong exercise training promotes the remodelling of the immune system and prostate signalome in a rat model of prostate carcinogenesis. GeroScience 2024; 46:817-840. [PMID: 37171559 PMCID: PMC10828357 DOI: 10.1007/s11357-023-00806-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/21/2023] [Indexed: 05/13/2023] Open
Abstract
This work aimed to understand how lifelong exercise training promotes the remodelling of the immune system and prostate signalome in a rat model of PCa. Fifty-five male Wistar rats were divided into four groups: control sedentary, control exercised, induced PCa sedentary and induced PCa exercised. Exercised animals were trained in a treadmill for 53 weeks. Pca induction consisted on the sequential administration of flutamide, N-methyl-N-nitrosourea and testosterone propionate implants. Serum concentrations of C-reactive protein (CRP) and tumor necrosis factor (TNF)-like weak inducer of apoptosis (TWEAK) were not different among groups. Peripheral levels of γδ T cells were higher in Pca exercised group than in the PCa sedentary group (p < 0.05). Exercise training also induced Oestrogen Receptor (ESR1) upregulation and Mitogen-activated Protein Kinase 13 (MAPK13) downregulation, changed the content of the phosphorylated (at Ser-104) form of this receptor (coded by the gene ESR1) and seemed to increase Erα phosphorylation and activity in exercised PCa rats when compared with sedentary PCa rats. Our data highlight the exercise-induced remodelling of peripheral lymphocyte subpopulations and lymphocyte infiltration in prostate tissue. Moreover, exercise training promotes the remodelling prostate signalome in this rat model of prostate carcinogenesis.
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Affiliation(s)
- Elisabete Nascimento-Gonçalves
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-Os-Montes and Alto Douro (UTAD), 5000-801, Vila Real, Portugal
- Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), UTAD, 5000-801, Vila Real, Portugal
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro (UA), 3810-193, Aveiro, Portugal
| | - Fernanda Seixas
- Animal and Veterinary Research Centre (CECAV), Associate Laboratory for Animal and Veterinary Science - AL4AnimalS, UTAD, 5000-801, Vila Real, Portugal
- Department of Veterinary Sciences, University of Trás-Os-Montes and Alto Douro, 5000-801, Vila Real, Portugal
| | - Carlos Palmeira
- Clinical Pathology Department, Portuguese Institute of Oncology of Porto, 4200-072, Porto, Portugal
- Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, 4200-072, Porto, Portugal
- School of Health Science Fernando Pessoa and FP-i3iD, 4200-253, Porto, Portugal
| | - Gabriela Martins
- Clinical Pathology Department, Portuguese Institute of Oncology of Porto, 4200-072, Porto, Portugal
- Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, 4200-072, Porto, Portugal
| | - Carolina Fonseca
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-Os-Montes and Alto Douro (UTAD), 5000-801, Vila Real, Portugal
- Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), UTAD, 5000-801, Vila Real, Portugal
| | - José Alberto Duarte
- CIAFEL, Research Centre in Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, 4200-450, Porto, Portugal
| | - Ana I Faustino-Rocha
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-Os-Montes and Alto Douro (UTAD), 5000-801, Vila Real, Portugal
- Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), UTAD, 5000-801, Vila Real, Portugal
- Department of Zootechnics, School of Sciences and Technology, University of Évora, 7004-516, Évora, Portugal
- Comprehensive Health Research Centre, 7004-516, Évora, Portugal
| | - Bruno Colaço
- Animal and Veterinary Research Centre (CECAV), Associate Laboratory for Animal and Veterinary Science - AL4AnimalS, UTAD, 5000-801, Vila Real, Portugal
- Department of Zootechnics, University of Trás-Os-Montes and Alto Douro, 5000-801, Vila Real, Portugal
| | - Maria João Pires
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-Os-Montes and Alto Douro (UTAD), 5000-801, Vila Real, Portugal
- Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), UTAD, 5000-801, Vila Real, Portugal
- Department of Veterinary Sciences, University of Trás-Os-Montes and Alto Douro, 5000-801, Vila Real, Portugal
| | - Maria João Neuparth
- Research Center in Physical Activity, Health and Leisure (CIAFEL)-Faculty of Sports-University of Porto (FADEUP), Portugal and Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, Portugal
- TOXRUN - Toxicology Research Unit, University Institute of Health Sciences, CESPU, CRL, 4585-116, Gandra, Portugal
| | - Daniel Moreira-Gonçalves
- Research Center in Physical Activity, Health and Leisure (CIAFEL)-Faculty of Sports-University of Porto (FADEUP), Portugal and Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, Portugal
| | - Margarida Fardilha
- Department of Medical Sciences, iBiMED - Institute of Biomedicine, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Magda C Henriques
- Department of Medical Sciences, iBiMED - Institute of Biomedicine, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Daniela Patrício
- Department of Medical Sciences, iBiMED - Institute of Biomedicine, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Steven Pelech
- Department of Medicine, University of British Columbia, Vancouver, B.C, Canada
- Kinexus Bioinformatics Corporation, Suite 1 - 8755 Ash Street, Vancouver, BC, V6P 6T3, Canada
| | - Rita Ferreira
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Paula A Oliveira
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-Os-Montes and Alto Douro (UTAD), 5000-801, Vila Real, Portugal.
- Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), UTAD, 5000-801, Vila Real, Portugal.
- Department of Veterinary Sciences, University of Trás-Os-Montes and Alto Douro, 5000-801, Vila Real, Portugal.
- Clinical Academic Center of Trás-Os-Montes and Alto Douro, University of Trás-Os-Montes and Alto Douro, 5000-801, Vila Real, Portugal.
- University of Trás-os-Montes and Alto Douro, Quinta dos Prados, 5001-801, Vila Real, Portugal.
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Becht A, Frączyk J, Waśko J, Menaszek E, Kajdanek J, Miłowska K, Kolesinska B. Selection of collagen IV fragments forming the outer sphere of the native protein: Assessment of biological activity for regenerative medicine. J Pept Sci 2024; 30:e3537. [PMID: 37607826 DOI: 10.1002/psc.3537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/02/2023] [Accepted: 08/02/2023] [Indexed: 08/24/2023]
Abstract
The aim of this research was to select the fragments that make up the outer layer of the collagen IV (COL4A6) protein and to assess their potential usefulness for regenerative medicine. It was expected that because protein-protein interactions take place via contact between external domains, the set of peptides forming the outer sphere of collagen IV will determine its interaction with other proteins. Cellulose-immobilized protein fragment libraries treated with polyclonal anti-collagen IV antibodies were used to select the peptides forming the outer sphere of collagen IV. In the first test, 33 peptides that strongly interacted with the polyclonal anti-collagen IV antibodies were selected from a library of non-overlapping fragments of collagen IV. The selected fragments of collagen IV (cleaved from the cellulose matrix) were tested for their cytotoxicity, their effects on cell viability and proliferation, and their impact on the formation of reactive oxygen species (ROS). The studies used RAW 264.7 mouse macrophage cells and Hs 680.Tr human fibroblasts. PrestoBlue, ToxiLight™, and ToxiLight 100% Lysis Control assays were conducted. The viability of fibroblasts cultured with the addition of increasing concentrations of the peptide mix did not show statistically significant differences from the control. Fragments 161-170, 221-230, 721-730, 1331-1340, 1521-1530, and 1661-1670 of COL4A6 were examined for cytotoxicity against BJ normal human foreskin fibroblasts. None of the collagen fragments were found to be cytotoxic. Further research is underway on the potential uses of collagen IV fragments in regenerative medicine.
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Affiliation(s)
- Angelika Becht
- Faculty of Chemistry, Institute of Organic Chemistry, Lodz University of Technology, Lodz, Poland
| | - Justyna Frączyk
- Faculty of Chemistry, Institute of Organic Chemistry, Lodz University of Technology, Lodz, Poland
| | - Joanna Waśko
- Faculty of Chemistry, Institute of Organic Chemistry, Lodz University of Technology, Lodz, Poland
| | - Elżbieta Menaszek
- Department of Cytobiology, Chair of Pharmacobiology, Faculty of Pharmacy, Jagiellonian University Collegium Medicum, Krakow, Poland
| | - Jakub Kajdanek
- Department of General Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland
| | - Katarzyna Miłowska
- Department of General Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland
| | - Beata Kolesinska
- Faculty of Chemistry, Institute of Organic Chemistry, Lodz University of Technology, Lodz, Poland
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26
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Fu X, Yuan Y, Qiu H, Suo H, Song Y, Li A, Zhang Y, Xiao C, Li Y, Dou L, Zhang Z, Cui F. AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks. Methods 2024; 222:142-151. [PMID: 38242383 DOI: 10.1016/j.ymeth.2024.01.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/04/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024] Open
Abstract
Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.
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Affiliation(s)
- Xiuhao Fu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Haoye Qiu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Haodong Suo
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yingying Song
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Anqi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yupeng Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Cuilin Xiao
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yazi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH 44106, USA
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
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27
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Lozano-Velasco E, Inácio JM, Sousa I, Guimarães AR, Franco D, Moura G, Belo JA. miRNAs in Heart Development and Disease. Int J Mol Sci 2024; 25:1673. [PMID: 38338950 PMCID: PMC10855082 DOI: 10.3390/ijms25031673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/25/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024] Open
Abstract
Cardiovascular diseases (CVD) are a group of disorders that affect the heart and blood vessels. They include conditions such as myocardial infarction, coronary artery disease, heart failure, arrhythmia, and congenital heart defects. CVDs are the leading cause of death worldwide. Therefore, new medical interventions that aim to prevent, treat, or manage CVDs are of prime importance. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the posttranscriptional level and play important roles in various biological processes, including cardiac development, function, and disease. Moreover, miRNAs can also act as biomarkers and therapeutic targets. In order to identify and characterize miRNAs and their target genes, scientists take advantage of computational tools such as bioinformatic algorithms, which can also assist in analyzing miRNA expression profiles, functions, and interactions in different cardiac conditions. Indeed, the combination of miRNA research and bioinformatic algorithms has opened new avenues for understanding and treating CVDs. In this review, we summarize the current knowledge on the roles of miRNAs in cardiac development and CVDs, discuss the challenges and opportunities, and provide some examples of recent bioinformatics for miRNA research in cardiovascular biology and medicine.
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Affiliation(s)
- Estefania Lozano-Velasco
- Cardiovascular Development Group, Department of Experimental Biology, University of Jaen, 23071 Jaen, Spain; (E.L.-V.); (D.F.)
| | - José Manuel Inácio
- Stem Cells and Development Laboratory, iNOVA4Health, NOVA Medical School|Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal;
| | - Inês Sousa
- Genome Medicine Lab, Department of Medical Sciences, Institute for Biomedicine–iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal; (I.S.); (A.R.G.); (G.M.)
| | - Ana Rita Guimarães
- Genome Medicine Lab, Department of Medical Sciences, Institute for Biomedicine–iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal; (I.S.); (A.R.G.); (G.M.)
| | - Diego Franco
- Cardiovascular Development Group, Department of Experimental Biology, University of Jaen, 23071 Jaen, Spain; (E.L.-V.); (D.F.)
| | - Gabriela Moura
- Genome Medicine Lab, Department of Medical Sciences, Institute for Biomedicine–iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal; (I.S.); (A.R.G.); (G.M.)
| | - José António Belo
- Stem Cells and Development Laboratory, iNOVA4Health, NOVA Medical School|Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal;
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Corda PO, Bollen M, Ribeiro D, Fardilha M. Emerging roles of the Protein Phosphatase 1 (PP1) in the context of viral infections. Cell Commun Signal 2024; 22:65. [PMID: 38267954 PMCID: PMC10807198 DOI: 10.1186/s12964-023-01468-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/30/2023] [Indexed: 01/26/2024] Open
Abstract
Protein Phosphatase 1 (PP1) is a major serine/threonine phosphatase in eukaryotes, participating in several cellular processes and metabolic pathways. Due to their low substrate specificity, PP1's catalytic subunits do not exist as free entities but instead bind to Regulatory Interactors of Protein Phosphatase One (RIPPO), which regulate PP1's substrate specificity and subcellular localization. Most RIPPOs bind to PP1 through combinations of short linear motifs (4-12 residues), forming highly specific PP1 holoenzymes. These PP1-binding motifs may, hence, represent attractive targets for the development of specific drugs that interfere with a subset of PP1 holoenzymes. Several viruses exploit the host cell protein (de)phosphorylation machinery to ensure efficient virus particle formation and propagation. While the role of many host cell kinases in viral life cycles has been extensively studied, the targeting of phosphatases by viral proteins has been studied in less detail. Here, we compile and review what is known concerning the role of PP1 in the context of viral infections and discuss how it may constitute a putative host-based target for the development of novel antiviral strategies.
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Affiliation(s)
- Pedro O Corda
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Mathieu Bollen
- Department of Cellular and Molecular Medicine, Laboratory of Biosignaling & Therapeutics, Katholieke Universiteit Leuven, Louvain, Belgium
| | - Daniela Ribeiro
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal.
| | - Margarida Fardilha
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal.
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29
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Bernett J, Blumenthal DB, List M. Cracking the black box of deep sequence-based protein-protein interaction prediction. Brief Bioinform 2024; 25:bbae076. [PMID: 38446741 PMCID: PMC10939362 DOI: 10.1093/bib/bbae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/09/2024] [Indexed: 03/08/2024] Open
Abstract
Identifying protein-protein interactions (PPIs) is crucial for deciphering biological pathways. Numerous prediction methods have been developed as cheap alternatives to biological experiments, reporting surprisingly high accuracy estimates. We systematically investigated how much reproducible deep learning models depend on data leakage, sequence similarities and node degree information, and compared them with basic machine learning models. We found that overlaps between training and test sets resulting from random splitting lead to strongly overestimated performances. In this setting, models learn solely from sequence similarities and node degrees. When data leakage is avoided by minimizing sequence similarities between training and test set, performances become random. Moreover, baseline models directly leveraging sequence similarity and network topology show good performances at a fraction of the computational cost. Thus, we advocate that any improvements should be reported relative to baseline methods in the future. Our findings suggest that predicting PPIs remains an unsolved task for proteins showing little sequence similarity to previously studied proteins, highlighting that further experimental research into the 'dark' protein interactome and better computational methods are needed.
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Affiliation(s)
- Judith Bernett
- Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof Forum 3, 85354, Freising, Germany
| | - David B Blumenthal
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Werner-von-Siemens-Str. 61, 91052, Erlangen, Germany
| | - Markus List
- Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof Forum 3, 85354, Freising, Germany
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30
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Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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31
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Zheng Y, Schupp JC, Adams T, Clair G, Justet A, Ahangari F, Yan X, Hansen P, Carlon M, Cortesi E, Vermant M, Vos R, De Sadeleer LJ, Rosas IO, Pineda R, Sembrat J, Königshoff M, McDonough JE, Vanaudenaerde BM, Wuyts WA, Kaminski N, Ding J. Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases. RESEARCH SQUARE 2023:rs.3.rs-3676579. [PMID: 38196613 PMCID: PMC10775382 DOI: 10.21203/rs.3.rs-3676579/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.
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Affiliation(s)
- Yumin Zheng
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, Canada
- Meakins-Christie Laboratories, Translational Research in Respiratory Diseases Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Jonas C. Schupp
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Taylor Adams
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Geremy Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Aurelien Justet
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Farida Ahangari
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Xiting Yan
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Paul Hansen
- Meakins-Christie Laboratories, Translational Research in Respiratory Diseases Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Marianne Carlon
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Emanuela Cortesi
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Marie Vermant
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Robin Vos
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Laurens J. De Sadeleer
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Ivan O Rosas
- Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Ricardo Pineda
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - John Sembrat
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Melanie Königshoff
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - John E. McDonough
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Bart M. Vanaudenaerde
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Wim A. Wuyts
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Jun Ding
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, Canada
- Meakins-Christie Laboratories, Translational Research in Respiratory Diseases Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
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Gkekas I, Vagiona AC, Pechlivanis N, Kastrinaki G, Pliatsika K, Iben S, Xanthopoulos K, Psomopoulos FE, Andrade-Navarro MA, Petrakis S. Intranuclear inclusions of polyQ-expanded ATXN1 sequester RNA molecules. Front Mol Neurosci 2023; 16:1280546. [PMID: 38125008 PMCID: PMC10730666 DOI: 10.3389/fnmol.2023.1280546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
Spinocerebellar ataxia type 1 (SCA1) is an autosomal dominant neurodegenerative disease caused by a trinucleotide (CAG) repeat expansion in the ATXN1 gene. It is characterized by the presence of polyglutamine (polyQ) intranuclear inclusion bodies (IIBs) within affected neurons. In order to investigate the impact of polyQ IIBs in SCA1 pathogenesis, we generated a novel protein aggregation model by inducible overexpression of the mutant ATXN1(Q82) isoform in human neuroblastoma SH-SY5Y cells. Moreover, we developed a simple and reproducible protocol for the efficient isolation of insoluble IIBs. Biophysical characterization showed that polyQ IIBs are enriched in RNA molecules which were further identified by next-generation sequencing. Finally, a protein interaction network analysis indicated that sequestration of essential RNA transcripts within ATXN1(Q82) IIBs may affect the ribosome resulting in error-prone protein synthesis and global proteome instability. These findings provide novel insights into the molecular pathogenesis of SCA1, highlighting the role of polyQ IIBs and their impact on critical cellular processes.
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Affiliation(s)
- Ioannis Gkekas
- Centre for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Nikolaos Pechlivanis
- Centre for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece
| | - Georgia Kastrinaki
- Aerosol and Particle Technology Laboratory, Centre for Research and Technology Hellas, Chemical Process and Energy Resources Institute, Thessaloniki, Greece
| | - Katerina Pliatsika
- Centre for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Sebastian Iben
- Department of Dermatology and Allergic Diseases, University of Ulm, Ulm, Germany
| | - Konstantinos Xanthopoulos
- Centre for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Fotis E. Psomopoulos
- Centre for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece
| | | | - Spyros Petrakis
- Centre for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece
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Sameni M, Mirmotalebisohi SA, Dadashkhan S, Ghani S, Abbasi M, Noori E, Zali H. COVID-19: A novel holistic systems biology approach to predict its molecular mechanisms (in vitro) and repurpose drugs. Daru 2023; 31:155-171. [PMID: 37597114 PMCID: PMC10624792 DOI: 10.1007/s40199-023-00471-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 07/13/2023] [Indexed: 08/21/2023] Open
Abstract
PURPOSE COVID-19 strangely kills some youth with no history of physical weakness, and in addition to the lungs, it may even directly harm other organs. Its complex mechanism has led to the loss of any significantly effective drug, and some patients with severe forms still die daily. Common methods for identifying disease mechanisms and drug design are often time-consuming or reductionist. Here, we use a novel holistic systems biology approach to predict its molecular mechanisms (in vitro), significant molecular relations with SARS, and repurpose drugs. METHODS We have utilized its relative phylogenic similarity to SARS. Using the available omics data for SARS and the fewer data for COVID-19 to decode the mechanisms and their significant relations, We applied the Cytoscape analyzer, MCODE, STRING, and DAVID tools to predict the topographically crucial molecules, clusters, protein interaction mappings, and functional analysis. We also applied a novel approach to identify the significant relations between the two infections using the Fischer exact test for MCODE clusters. We then constructed and analyzed a drug-gene network using PharmGKB and DrugBank (retrieved using the dgidb). RESULTS Some of the shared identified crucial molecules, BPs and pathways included Kaposi sarcoma-associated herpesvirus infection, Influenza A, and NOD-like receptor signaling pathways. Besides, our identified crucial molecules specific to host response against SARS-CoV-2 included FGA, BMP4, PRPF40A, and IFI16. CONCLUSION We also introduced seven new repurposed candidate drugs based on the drug-gene network analysis for the identified crucial molecules. Therefore, we suggest that our newly recommended repurposed drugs be further investigated in Vitro and in Vivo against COVID-19.
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Affiliation(s)
- Marzieh Sameni
- Student Research Committee, Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Amir Mirmotalebisohi
- Student Research Committee, Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sadaf Dadashkhan
- Molecular Medicine Research Center, Universitätsklinikum Jena, Jena, Germany
| | - Sepideh Ghani
- Student Research Committee, Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Abbasi
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
- Zhino-Gene Research Services Co., Tehran, Iran
| | - Effat Noori
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hakimeh Zali
- Proteomics Research Center, Shahid Beheshti University of Medical Science, Tehran, Iran.
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Marzi SJ, Schilder BM, Nott A, Frigerio CS, Willaime-Morawek S, Bucholc M, Hanger DP, James C, Lewis PA, Lourida I, Noble W, Rodriguez-Algarra F, Sharif JA, Tsalenchuk M, Winchester LM, Yaman Ü, Yao Z, Ranson JM, Llewellyn DJ. Artificial intelligence for neurodegenerative experimental models. Alzheimers Dement 2023; 19:5970-5987. [PMID: 37768001 DOI: 10.1002/alz.13479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.
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Affiliation(s)
- Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Alexi Nott
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | | | | | - Magda Bucholc
- School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Diane P Hanger
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Patrick A Lewis
- Royal Veterinary College, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | | | - Wendy Noble
- Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | | | - Jalil-Ahmad Sharif
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Maria Tsalenchuk
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | | | - Ümran Yaman
- UK Dementia Research Institute at UCL, London, UK
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
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Kosoglu K, Aydin Z, Tuncbag N, Gursoy A, Keskin O. Structural coverage of the human interactome. Brief Bioinform 2023; 25:bbad496. [PMID: 38180828 PMCID: PMC10768791 DOI: 10.1093/bib/bbad496] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 01/07/2024] Open
Abstract
Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.
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Affiliation(s)
- Kayra Kosoglu
- Computational Sciences and Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Zeynep Aydin
- Computational Sciences and Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Nurcan Tuncbag
- School of Medicine, Koc University, 34450 Istanbul, Turkey
- Department of Chemical and Biological Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
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Yavuz BR, Arici MK, Demirel HC, Tsai CJ, Jang H, Nussinov R, Tuncbag N. Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome. NPJ Genom Med 2023; 8:37. [PMID: 37925498 PMCID: PMC10625621 DOI: 10.1038/s41525-023-00377-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 10/02/2023] [Indexed: 11/06/2023] Open
Abstract
Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.
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Affiliation(s)
- Bengi Ruken Yavuz
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, 21702, USA
| | - M Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey
| | - Habibe Cansu Demirel
- Graduate School of Sciences and Engineering, Koc University, Istanbul, 34450, Turkey
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, 21702, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, 21702, USA.
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey.
- School of Medicine, Koc University, Istanbul, 34450, Turkey.
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Turkey.
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Rajarajan S, Snijesh VP, Anupama CE, Nair MG, Mavatkar AD, Naidu CM, Patil S, Nimbalkar VP, Alexander A, Pillai M, Jolly MK, Sabarinathan R, Ramesh RS, Bs S, Prabhu JS. An androgen receptor regulated gene score is associated with epithelial to mesenchymal transition features in triple negative breast cancers. Transl Oncol 2023; 37:101761. [PMID: 37603927 PMCID: PMC10465938 DOI: 10.1016/j.tranon.2023.101761] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/13/2023] [Accepted: 08/07/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Androgen receptor (AR) is considered a marker of better prognosis in hormone receptor positive breast cancers (BC), however, its role in triple negative breast cancer (TNBC) is controversial. This may be attributed to intrinsic molecular differences or scoring methods for AR positivity. We derived AR regulated gene score and examined its utility in BC subtypes. METHODS AR regulated genes were derived by applying a bioinformatic pipeline on publicly available microarray data sets of AR+ BC cell lines and gene score was calculated as average expression of six AR regulated genes. Tumors were divided into AR high and low based on gene score and associations with clinical parameters, circulating androgens, survival and epithelial to mesenchymal transition (EMT) markers were examined, further evaluated in invitro models and public datasets. RESULTS 53% (133/249) tumors were classified as AR gene score high and were associated with significantly better clinical parameters, disease-free survival (86.13 vs 72.69 months, log rank p = 0.032) when compared to AR low tumors. 36% of TNBC (N = 66) were AR gene score high with higher expression of EMT markers (p = 0.024) and had high intratumoral levels of 5α-reductase, enzyme involved in intracrine androgen metabolism. In MDA-MB-453 treated with dihydrotestosterone, SLUG expression increased, E-cadherin decreased with increase in migration and these changes were reversed with bicalutamide. Similar results were obtained in public datasets. CONCLUSION Deciphering the role of AR in BC is difficult based on AR protein levels alone. Our results support the context dependent function of AR in driving better prognosis in ER positive tumors and EMT features in TNBC tumors.
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Affiliation(s)
- Savitha Rajarajan
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India; Centre for Doctoral Studies, Manipal Academy of Higher Education (MAHE), Manipal, India
| | - V P Snijesh
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India; Centre for Doctoral Studies, Manipal Academy of Higher Education (MAHE), Manipal, India
| | - C E Anupama
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India
| | - Madhumathy G Nair
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India
| | - Apoorva D Mavatkar
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India
| | - Chandrakala M Naidu
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India
| | - Sharada Patil
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India
| | - Vidya P Nimbalkar
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India
| | - Annie Alexander
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India
| | - Maalavika Pillai
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, India
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, India
| | | | - Rakesh S Ramesh
- Department of Surgical Oncology, St. John's Medical College, Bengaluru, India
| | - Srinath Bs
- Department of Surgery, Sri Shankara Cancer Hospital and Research Centre, Bangalore, India
| | - Jyothi S Prabhu
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bengaluru, India.
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Lamberto F, Shashikadze B, Elkhateib R, Lombardo SD, Horánszky A, Balogh A, Kistamás K, Zana M, Menche J, Fröhlich T, Dinnyés A. Low-dose Bisphenol A exposure alters the functionality and cellular environment in a human cardiomyocyte model. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 335:122359. [PMID: 37567409 DOI: 10.1016/j.envpol.2023.122359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/26/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Early embryonic development represents a sensitive time-window during which the foetus might be vulnerable to the exposure of environmental contaminants, potentially leading to heart diseases also later in life. Bisphenol A (BPA), a synthetic chemical widely used in plastics manufacturing, has been associated with heart developmental defects, even in low concentrations. This study aims to investigate the effects of environmentally relevant doses of BPA on developing cardiomyocytes using a human induced pluripotent stem cell (hiPSC)-derived model. Firstly, a 2D in vitro differentiation system to obtain cardiomyocytes from hiPSCs (hiPSC-CMs) have been established and characterised to provide a suitable model for the early stages of cardiac development. Then, the effects of a repeated BPA exposure, starting from the undifferentiated stage throughout the differentiation process, were evaluated. The chemical significantly decreased the beat rate of hiPSC-CMs, extending the contraction and relaxation time in a dose-dependent manner. Quantitative proteomics analysis revealed a high abundance of basement membrane (BM) components (e.g., COL4A1, COL4A2, LAMC1, NID2) and a significant increase in TNNC1 and SERBP1 proteins in hiPSC-CMs treated with BPA. Network analysis of proteomics data supported altered extracellular matrix remodelling and provided a disease-gene association with well-known pathological conditions of the heart. Furthermore, upon hypoxia-reoxygenation challenge, hiPSC-CMs treated with BPA showed higher rate of apoptotic events. Taken together, our results revealed that a long-term treatment, even with low doses of BPA, interferes with hiPSC-CMs functionality and alters the surrounding cellular environment, providing new insights about diseases that might arise upon the toxin exposure. Our study contributes to the current understanding of BPA effects on developing human foetal cardiomyocytes, in correlation with human clinical observations and animal studies, and it provides a suitable model for New Approach Methodologies (NAMs) for environmental chemical hazard and risk assessment.
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Affiliation(s)
- Federica Lamberto
- BioTalentum Ltd., Aulich Lajos Str. 26, Gödöllő, H-2100, Hungary; Department of Physiology and Animal Health, Institute of Physiology and Animal Nutrition, Hungarian University of Agriculture and Life Sciences, Páter Károly Str. 1, H-2100, Gödöllő, Hungary
| | - Bachuki Shashikadze
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377, Munich, Germany
| | - Radwa Elkhateib
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377, Munich, Germany
| | - Salvo Danilo Lombardo
- Max Perutz Labs, Vienna Biocenter Campus (VBC), 1030, Vienna, Austria; Department of Structural and Computational Biology, Center for Molecular Biology, University of Vienna, 1030, Vienna, Austria; CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090, Vienna, Austria
| | - Alex Horánszky
- BioTalentum Ltd., Aulich Lajos Str. 26, Gödöllő, H-2100, Hungary; Department of Physiology and Animal Health, Institute of Physiology and Animal Nutrition, Hungarian University of Agriculture and Life Sciences, Páter Károly Str. 1, H-2100, Gödöllő, Hungary
| | - Andrea Balogh
- BioTalentum Ltd., Aulich Lajos Str. 26, Gödöllő, H-2100, Hungary
| | - Kornél Kistamás
- BioTalentum Ltd., Aulich Lajos Str. 26, Gödöllő, H-2100, Hungary
| | - Melinda Zana
- BioTalentum Ltd., Aulich Lajos Str. 26, Gödöllő, H-2100, Hungary
| | - Jörg Menche
- Max Perutz Labs, Vienna Biocenter Campus (VBC), 1030, Vienna, Austria; Department of Structural and Computational Biology, Center for Molecular Biology, University of Vienna, 1030, Vienna, Austria; CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090, Vienna, Austria; Faculty of Mathematics, University of Vienna, 1090, Vienna, Austria
| | - Thomas Fröhlich
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377, Munich, Germany
| | - András Dinnyés
- BioTalentum Ltd., Aulich Lajos Str. 26, Gödöllő, H-2100, Hungary; Department of Physiology and Animal Health, Institute of Physiology and Animal Nutrition, Hungarian University of Agriculture and Life Sciences, Páter Károly Str. 1, H-2100, Gödöllő, Hungary; Department of Cell Biology and Molecular Medicine, University of Szeged, H-6720, Szeged, Hungary.
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Halsana AA, Chakroborty T, Halder AK, Basu S. DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions. IEEE Trans Nanobioscience 2023; 22:904-911. [PMID: 37028059 DOI: 10.1109/tnb.2023.3251192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the interacting protein pairs for PPI prediction. A colour encoding scheme has been introduced to embed the bigram interaction possibilities of Amino Acids into RGB colour space to enhance the learning and prediction task. The DensePPI model is trained on 5.5 million sub-images of size 128×128 generated from nearly 36,000 interacting and 36,000 non-interacting benchmark protein pairs. The performance is evaluated on independent datasets from five different organisms; Caenorhabditis elegans, Escherichia coli, Helicobacter Pylori, Homo sapiens and Mus Musculus. The proposed model achieves an average prediction accuracy score of 99.95% on these datasets, considering inter-species and intra-species interactions. The performance of DensePPI is compared with the state-of-the-art methods and outperforms those approaches in different evaluation metrics. Improved performance of DensePPI indicates the efficiency of the image-based encoding strategy of sequence information with the deep learning architecture in PPI prediction. The enhanced performance on diverse test sets shows that the DensePPI is significant for intra-species interaction prediction and cross-species interactions. The dataset, supplementary file, and the developed models are available at https://github.com/Aanzil/DensePPI for academic use only.
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40
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Li Q, Wu K, Zhang Y, Liu Y, Wang Y, Chen Y, Sun S, Duan C. Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks. J Cancer Res Clin Oncol 2023; 149:11263-11278. [PMID: 37358667 DOI: 10.1007/s00432-023-04989-4] [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: 04/25/2023] [Accepted: 06/15/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVE To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment. METHODS Genes related to HBV-HCC were selected through literature screening and protein-protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan-Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors. RESULTS The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan-Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level. CONCLUSION PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.
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Affiliation(s)
- Qingxiu Li
- Department of Cell Biology and Medical Genetics, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Kejia Wu
- Department of Cell Biology and Medical Genetics, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Yiqi Zhang
- Department of Cell Biology and Medical Genetics, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Yuxin Liu
- Department of Cell Biology and Medical Genetics, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Yalan Wang
- Department of Cell Biology and Medical Genetics, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Yong Chen
- Department of Hepatobillary Surgery, The First Affiliated Hospital, Chongqing Medical University, Chongqing, 400016, China
| | - Shuangling Sun
- Chongqing Medical and Pharmaceutical College, No. 82, University Town Middle Road, Shapingba District, Chongqing, 400016, China
| | - Changzhu Duan
- Department of Cell Biology and Medical Genetics, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China.
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Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, Burren OS, Davitte J, Ferber KL, Gillies CE, Hedman ÅK, Hu S, Lin T, Mikkilineni R, Pendergrass RK, Pickering C, Prins B, Baird D, Chen CY, Ward LD, Deaton AM, Welsh S, Willis CM, Lehner N, Arnold M, Wörheide MA, Suhre K, Kastenmüller G, Sethi A, Cule M, Raj A, Burkitt-Gray L, Melamud E, Black MH, Fauman EB, Howson JMM, Kang HM, McCarthy MI, Nioi P, Petrovski S, Scott RA, Smith EN, Szalma S, Waterworth DM, Mitnaul LJ, Szustakowski JD, Gibson BW, Miller MR, Whelan CD. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023; 622:329-338. [PMID: 37794186 PMCID: PMC10567551 DOI: 10.1038/s41586-023-06592-6] [Citation(s) in RCA: 84] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/31/2023] [Indexed: 10/06/2023]
Abstract
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.
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Affiliation(s)
- Benjamin B Sun
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
| | - Joshua Chiou
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Matthew Traylor
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Tom G Richardson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Genomic Sciences, GlaxoSmithKline, Stevenage, UK
| | | | | | - Chloe Robins
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Liping Hou
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | | | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen, Cambridge, MA, USA
| | | | - Åsa K Hedman
- External Science and Innovation Target Sciences, Worldwide Research, Development and Medical, Pfizer, Stockholm, Sweden
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Tinchi Lin
- Analytics and Data Sciences, Biogen, Cambridge, MA, USA
| | - Rajesh Mikkilineni
- Data Science Institute, Takeda Development Center Americas, Cambridge, MA, USA
| | | | | | - Bram Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Denis Baird
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Lucas D Ward
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Aimee M Deaton
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | | | - Carissa M Willis
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Nick Lehner
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Anil Raj
- Calico Life Sciences, San Francisco, CA, USA
| | | | | | - Mary Helen Black
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Joanna M M Howson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Paul Nioi
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | | | - Erin N Smith
- Takeda Development Center Americas, San Diego, CA, USA
| | - Sándor Szalma
- Takeda Development Center Americas, San Diego, CA, USA
| | | | | | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Christopher D Whelan
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
- Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
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Kelly K, Lewis PA, Plun-Favreau H, Manzoni C. Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial and nuclear biology. Mol Omics 2023; 19:668-679. [PMID: 37427757 DOI: 10.1039/d2mo00325b] [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: 07/11/2023]
Abstract
Whilst the majority of Parkinson's Disease (PD) cases are sporadic, much of our understanding of the pathophysiological basis of the disease can be traced back to the study of rare, monogenic forms of PD. In the past decade, the availability of genome-wide association studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring increased risk of developing PD across the population. A recent mitophagy screening assay of GWAS candidates has functionally implicated the non-specific lethal (NSL) complex in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the proteome of the NSL complex, to unpick its relevance to PD pathogenesis. The NSL interactome has been built, using 3 online tools: PINOT, HIPPIE and MIST, to mine curated, literature-derived protein-protein interaction (PPI) data. We built (i) the 'mitochondrial' NSL interactome exploring its relevance to PD genetics and (ii) the PD-oriented NSL interactome to uncover biological pathways underpinning the NSL/PD association. In this study, we find the mitochondrial NSL interactome to be significantly enriched for the protein products of PD-associated genes, including the Mendelian PD genes LRRK2 and VPS35. In addition, we find nuclear processes to be amongst those most significantly enriched within the PD-associated NSL interactome. These findings strengthen the role of the NSL complex in sporadic and familial PD, mediated by both its mitochondrial and nuclear functions.
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Affiliation(s)
- Katie Kelly
- UCL Queen Square Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Patrick A Lewis
- UCL Queen Square Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
- Royal Veterinary College, University of London, Royal College Street, Camden, NW1 0TU, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Helene Plun-Favreau
- UCL Queen Square Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Claudia Manzoni
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- UCL School of Pharmacy, Brunswick Square, London, WC1N 1AX, UK.
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43
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Mohseni Behbahani Y, Saighi P, Corsi F, Laine E, Carbone A. LEVELNET to visualize, explore, and compare protein-protein interaction networks. Proteomics 2023; 23:e2200159. [PMID: 37403279 DOI: 10.1002/pmic.202200159] [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: 07/16/2022] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 07/06/2023]
Abstract
Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein-protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.
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Affiliation(s)
- Yasser Mohseni Behbahani
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Paul Saighi
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Flavia Corsi
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
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44
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Guthrie J, Ko¨stel Bal S, Lombardo SD, Mu¨ller F, Sin C, Hu¨tter CV, Menche J, Boztug K. AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation. SCIENCE ADVANCES 2023; 9:eadg6375. [PMID: 37656781 PMCID: PMC10848965 DOI: 10.1126/sciadv.adg6375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/01/2023] [Indexed: 09/03/2023]
Abstract
Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.
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Affiliation(s)
- Julia Guthrie
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Sevgi Ko¨stel Bal
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
| | - Salvo Danilo Lombardo
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Felix Mu¨ller
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Celine Sin
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Christiane V. R. Hu¨tter
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna BioCenter, A-1030 Vienna, Austria
| | - Jo¨rg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria
| | - Kaan Boztug
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
- St. Anna Children’s Hospital, Kinderspitalgasse 6, A-1090, Vienna, Austria
- Medical University of Vienna, Department of Pediatrics and Adolescent Medicine, Währinger Gürtel 18-20, A-1090 Vienna, Austria
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Horánszky A, Shashikadze B, Elkhateib R, Lombardo SD, Lamberto F, Zana M, Menche J, Fröhlich T, Dinnyés A. Proteomics and disease network associations evaluation of environmentally relevant Bisphenol A concentrations in a human 3D neural stem cell model. Front Cell Dev Biol 2023; 11:1236243. [PMID: 37664457 PMCID: PMC10472293 DOI: 10.3389/fcell.2023.1236243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Bisphenol A (BPA) exposure is associated with a plethora of neurodevelopmental abnormalities and brain disorders. Previous studies have demonstrated BPA-induced perturbations to critical neural stem cell (NSC) characteristics, such as proliferation and differentiation, although the underlying molecular mechanisms remain under debate. The present study evaluated the effects of a repeated-dose exposure of environmentally relevant BPA concentrations during the in vitro 3D neural induction of human induced pluripotent stem cells (hiPSCs), emulating a chronic exposure scenario. Firstly, we demonstrated that our model is suitable for NSC differentiation during the early stages of embryonic brain development. Our morphological image analysis showed that BPA exposure at 0.01, 0.1 and 1 µM decreased the average spheroid size by day 21 (D21) of the neural induction, while no effect on cell viability was detected. No alteration to the rate of the neural induction was observed based on the expression of key neural lineage and neuroectodermal transcripts. Quantitative proteomics at D21 revealed several differentially abundant proteins across all BPA-treated groups with important functions in NSC proliferation and maintenance (e.g., FABP7, GPC4, GAP43, Wnt-8B, TPPP3). Additionally, a network analysis demonstrated alterations to the glycolytic pathway, potentially implicating BPA-induced changes to glycolytic signalling in NSC proliferation impairments, as well as the pathophysiology of brain disorders including intellectual disability, autism spectrum disorders, and amyotrophic lateral sclerosis (ALS). This study enhances the current understanding of BPA-related NSC aberrations based mostly on acute, often high dose exposures of rodent in vivo and in vitro models and human GWAS data in a novel human 3D cell-based model with real-life scenario relevant prolonged and low-level exposures, offering further mechanistic insights into the ramifications of BPA exposure on the developing human brain and consequently, later life neurological disorders.
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Affiliation(s)
- Alex Horánszky
- BioTalentum Ltd., Gödöllő, Hungary
- Department of Physiology and Animal Health, Institute of Physiology and Animal Nutrition, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | - Bachuki Shashikadze
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Munich, Germany
| | - Radwa Elkhateib
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Munich, Germany
| | - Salvo Danilo Lombardo
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna, Austria
- Department of Structural and Computational Biology, Center for Molecular Biology, University of Vienna, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Federica Lamberto
- BioTalentum Ltd., Gödöllő, Hungary
- Department of Physiology and Animal Health, Institute of Physiology and Animal Nutrition, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | | | - Jörg Menche
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna, Austria
- Department of Structural and Computational Biology, Center for Molecular Biology, University of Vienna, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Faculty of Mathematics, University of Vienna, Vienna, Austria
| | - Thomas Fröhlich
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Munich, Germany
| | - András Dinnyés
- BioTalentum Ltd., Gödöllő, Hungary
- Department of Physiology and Animal Health, Institute of Physiology and Animal Nutrition, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
- Department of Cell Biology and Molecular Medicine, University of Szeged, Szeged, Hungary
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Vadnala RN, Hannenhalli S, Narlikar L, Siddharthan R. Transcription factors organize into functional groups on the linear genome and in 3D chromatin. Heliyon 2023; 9:e18211. [PMID: 37520992 PMCID: PMC10382302 DOI: 10.1016/j.heliyon.2023.e18211] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 08/01/2023] Open
Abstract
Transcription factors (TFs) and their binding sites have evolved to interact cooperatively or competitively with each other. Here we examine in detail, across multiple cell lines, such cooperation or competition among TFs both in sequential and spatial proximity (using chromatin conformation capture assays), considering in vivo binding data as well as TF binding motifs in DNA. We ascertain significantly co-occurring ("attractive") or avoiding ("repulsive") TF pairs using robust randomized models that retain the essential characteristics of the experimental data. Across human cell lines TFs organize into two groups, with intra-group attraction and inter-group repulsion. This is true for both sequential and spatial proximity, and for both in vivo binding and sequence motifs. Attractive TF pairs exhibit significantly more physical interactions suggesting an underlying mechanism. The two TF groups differ significantly in their genomic and network properties, as well in their function-while one group regulates housekeeping function, the other potentially regulates lineage-specific functions, that are disrupted in cancer. Weaker binding sites tend to occur in spatially interacting regions of the genome. Our results suggest that a complex pattern of spatial cooperativity of TFs and chromatin has evolved with the genome to support housekeeping and lineage-specific functions.
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Affiliation(s)
- Rakesh Netha Vadnala
- The Institute of Mathematical Sciences, Chennai, India
- Homi Bhabha National Institute, Mumbai, India
| | | | - Leelavati Narlikar
- Department of Data Science, Indian Institute of Science Education and Research, Pune, India
| | - Rahul Siddharthan
- The Institute of Mathematical Sciences, Chennai, India
- Homi Bhabha National Institute, Mumbai, India
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47
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Ershov P, Yablokov E, Mezentsev Y, Ivanov A. Uncharacterized Proteins CxORFx: Subinteractome Analysis and Prognostic Significance in Cancers. Int J Mol Sci 2023; 24:10190. [PMID: 37373333 DOI: 10.3390/ijms241210190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Functions of about 10% of all the proteins and their associations with diseases are poorly annotated or not annotated at all. Among these proteins, there is a group of uncharacterized chromosome-specific open-reading frame genes (CxORFx) from the 'Tdark' category. The aim of the work was to reveal associations of CxORFx gene expression and ORF proteins' subinteractomes with cancer-driven cellular processes and molecular pathways. We performed systems biology and bioinformatic analysis of 219 differentially expressed CxORFx genes in cancers, an estimation of prognostic significance of novel transcriptomic signatures and analysis of subinteractome composition using several web servers (GEPIA2, KMplotter, ROC-plotter, TIMER, cBioPortal, DepMap, EnrichR, PepPSy, cProSite, WebGestalt, CancerGeneNet, PathwAX II and FunCoup). The subinteractome of each ORF protein was revealed using ten different data sources on physical protein-protein interactions (PPIs) to obtain representative datasets for the exploration of possible cellular functions of ORF proteins through a spectrum of neighboring annotated protein partners. A total of 42 out of 219 presumably cancer-associated ORF proteins and 30 cancer-dependent binary PPIs were found. Additionally, a bibliometric analysis of 204 publications allowed us to retrieve biomedical terms related to ORF genes. In spite of recent progress in functional studies of ORF genes, the current investigations aim at finding out the prognostic value of CxORFx expression patterns in cancers. The results obtained expand the understanding of the possible functions of the poorly annotated CxORFx in the cancer context.
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Affiliation(s)
- Pavel Ershov
- Institute of Biomedical Chemistry, Moscow 119121, Russia
| | | | - Yuri Mezentsev
- Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Alexis Ivanov
- Institute of Biomedical Chemistry, Moscow 119121, Russia
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48
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Mo J, Li Z, Chen H, Lu Z, Ding B, Yuan X, Liu Y, Zhu W. Network medicine framework identified drug-repurposing opportunities of pharmaco-active compounds of Angelica acutiloba (Siebold & Zucc.) Kitag. for skin aging. Aging (Albany NY) 2023; 15:5144-5163. [PMID: 37310405 PMCID: PMC10292898 DOI: 10.18632/aging.204789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/15/2023] [Indexed: 06/14/2023]
Abstract
Increasing incidence of skin aging has highlighted the importance of identifying effective drugs with repurposed opportunities for skin aging. We aimed to identify pharmaco-active compounds with drug-repurposing opportunities for skin aging from Angelica acutiloba (Siebold & Zucc.) Kitag. (AAK). The proximity of network medicine framework (NMF) firstly identified 8 key AAK compounds with repurposed opportunities for skin aging, which may exert by regulating 29 differentially expressed genes (DGEs) of skin aging, including 13 up-regulated targets and 16 down-regulated targets. Connectivity MAP (cMAP) analysis revealed 8 key compounds were involved in regulating the process of cell proliferation and apoptosis, mitochondrial energy metabolism and oxidative stress of skin aging. Molecular docking analysis showed that 8 key compounds had a high docked ability with AR, BCHE, HPGD and PI3, which were identified as specific biomarker for the diagnosis of skin aging. Finally, the mechanisms of these key compounds were predicted to be involved in inhibiting autophagy pathway and activating Phospholipase D signaling pathway. In conclusion, this study firstly elucidated the drug-repurposing opportunities of AAK compounds for skin aging, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.
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Affiliation(s)
- Jiaxin Mo
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Zunjiang Li
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Hankun Chen
- Guangzhou Qinglan Biotechnology Co. Ltd., Guangzhou Province 515000, China
| | - Zhongyu Lu
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Banghan Ding
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
| | - Xiaohong Yuan
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
| | - Yuan Liu
- Guangzhou Huamiao Biotechnology Research Institute Co. Ltd., Guangzhou Province 510000, China
| | - Wei Zhu
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
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49
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Sousa A, Rocha S, Vieira J, Reboiro-Jato M, López-Fernández H, Vieira CP. On the identification of potential novel therapeutic targets for spinocerebellar ataxia type 1 (SCA1) neurodegenerative disease using EvoPPI3. J Integr Bioinform 2023; 20:jib-2022-0056. [PMID: 36848492 PMCID: PMC10561075 DOI: 10.1515/jib-2022-0056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 11/26/2022] [Indexed: 03/01/2023] Open
Abstract
EvoPPI (http://evoppi.i3s.up.pt), a meta-database for protein-protein interactions (PPI), has been upgraded (EvoPPI3) to accept new types of data, namely, PPI from patients, cell lines, and animal models, as well as data from gene modifier experiments, for nine neurodegenerative polyglutamine (polyQ) diseases caused by an abnormal expansion of the polyQ tract. The integration of the different types of data allows users to easily compare them, as here shown for Ataxin-1, the polyQ protein involved in spinocerebellar ataxia type 1 (SCA1) disease. Using all available datasets and the data here obtained for Drosophila melanogaster wt and exp Ataxin-1 mutants (also available at EvoPPI3), we show that, in humans, the Ataxin-1 network is much larger than previously thought (380 interactors), with at least 909 interactors. The functional profiling of the newly identified interactors is similar to the ones already reported in the main PPI databases. 16 out of 909 interactors are putative novel SCA1 therapeutic targets, and all but one are already being studied in the context of this disease. The 16 proteins are mainly involved in binding and catalytic activity (mainly kinase activity), functional features already thought to be important in the SCA1 disease.
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Affiliation(s)
- André Sousa
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
| | - Sara Rocha
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
| | - Jorge Vieira
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
- Instituto de Biologia Molecular e Celular (IBMC), Rua Alfredo Allen, 208, 4200-135Porto, Portugal
| | - Miguel Reboiro-Jato
- Department of Computer Science, CINBIO, Universidade de Vigo, ESEI – Escuela Superior de Ingeniería Informática, 32004Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Hugo López-Fernández
- Department of Computer Science, CINBIO, Universidade de Vigo, ESEI – Escuela Superior de Ingeniería Informática, 32004Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Cristina P. Vieira
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
- Instituto de Biologia Molecular e Celular (IBMC), Rua Alfredo Allen, 208, 4200-135Porto, Portugal
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50
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Federico A, Kern J, Varelas X, Monti S. Structure Learning for Gene Regulatory Networks. PLoS Comput Biol 2023; 19:e1011118. [PMID: 37200395 DOI: 10.1371/journal.pcbi.1011118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/31/2023] [Accepted: 04/20/2023] [Indexed: 05/20/2023] Open
Abstract
Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput "omics" data typically available. To overcome this challenge, often referred to as the "small n, large p problem," we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE-Structure Learning for Hierarchical Networks-a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.
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Affiliation(s)
- Anthony Federico
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
| | - Joseph Kern
- Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Xaralabos Varelas
- Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Stefano Monti
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
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