1
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Zheng H, Xu L, Xie H, Xie J, Ma Y, Hu Y, Wu L, Chen J, Wang M, Yi Y, Huang Y, Wang D. RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature. Comput Struct Biotechnol J 2024; 23:1469-1476. [PMID: 38623560 PMCID: PMC11016866 DOI: 10.1016/j.csbj.2024.03.017] [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: 01/24/2024] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/17/2024] Open
Abstract
RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.
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Affiliation(s)
- Hailong Zheng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Linfu Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Hailong Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jiajing Xie
- National Institute for Data Science in Health and Medicine, Xiamen University, 361102 Xiamen, China
| | - Yapeng Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yongfei Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Le Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jia Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Meiyi Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Ying Yi
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yan Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, 510515, Guangzhou, China
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2
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Ricard-Blum S, Vivès RR, Schaefer L, Götte M, Merline R, Passi A, Heldin P, Magalhães A, Reis CA, Skandalis SS, Karamanos NK, Perez S, Nikitovic D. A biological guide to glycosaminoglycans: current perspectives and pending questions. FEBS J 2024; 291:3331-3366. [PMID: 38500384 DOI: 10.1111/febs.17107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/08/2024] [Accepted: 02/20/2024] [Indexed: 03/20/2024]
Abstract
Mammalian glycosaminoglycans (GAGs), except hyaluronan (HA), are sulfated polysaccharides that are covalently attached to core proteins to form proteoglycans (PGs). This article summarizes key biological findings for the most widespread GAGs, namely HA, chondroitin sulfate/dermatan sulfate (CS/DS), keratan sulfate (KS), and heparan sulfate (HS). It focuses on the major processes that remain to be deciphered to get a comprehensive view of the mechanisms mediating GAG biological functions. They include the regulation of GAG biosynthesis and postsynthetic modifications in heparin (HP) and HS, the composition, heterogeneity, and function of the tetrasaccharide linkage region and its role in disease, the functional characterization of the new PGs recently identified by glycoproteomics, the selectivity of interactions mediated by GAG chains, the display of GAG chains and PGs at the cell surface and their impact on the availability and activity of soluble ligands, and on their move through the glycocalyx layer to reach their receptors, the human GAG profile in health and disease, the roles of GAGs and particular PGs (syndecans, decorin, and biglycan) involved in cancer, inflammation, and fibrosis, the possible use of GAGs and PGs as disease biomarkers, and the design of inhibitors targeting GAG biosynthetic enzymes and GAG-protein interactions to develop novel therapeutic approaches.
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Affiliation(s)
- Sylvie Ricard-Blum
- Univ Lyon 1, ICBMS, UMR 5246 University Lyon 1 - CNRS, Villeurbanne cedex, France
| | | | - Liliana Schaefer
- Institute of Pharmacology and Toxicology, Goethe University, Frankfurt, Germany
| | - Martin Götte
- Department of Gynecology and Obstetrics, Münster University Hospital, Germany
| | - Rosetta Merline
- Institute of Pharmacology and Toxicology, Goethe University, Frankfurt, Germany
| | | | - Paraskevi Heldin
- Department of Medical Biochemistry and Microbiology, Uppsala University, Sweden
| | - Ana Magalhães
- Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal
- ICBAS - Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal
| | - Celso A Reis
- Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal
- ICBAS - Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal
| | - Spyros S Skandalis
- Biochemistry, Biochemical Analysis & Matrix Pathobiology Res. Group, Laboratory of Biochemistry, Department of Chemistry, University of Patras, Greece
| | - Nikos K Karamanos
- Biochemistry, Biochemical Analysis & Matrix Pathobiology Res. Group, Laboratory of Biochemistry, Department of Chemistry, University of Patras, Greece
| | - Serge Perez
- Centre de Recherche sur les Macromolécules Végétales, University of Grenoble-Alpes, CNRS, France
| | - Dragana Nikitovic
- Laboratory of Histology-Embryology, School of Medicine, University of Crete, Heraklion, Greece
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3
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Razuvayevskaya O, Lopez I, Dunham I, Ochoa D. Genetic factors associated with reasons for clinical trial stoppage. Nat Genet 2024:10.1038/s41588-024-01854-z. [PMID: 39075208 DOI: 10.1038/s41588-024-01854-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/02/2024] [Indexed: 07/31/2024]
Abstract
Many drug discovery projects are started but few progress fully through clinical trials to approval. Previous work has shown that human genetics support for the therapeutic hypothesis increases the chance of trial progression. Here, we applied natural language processing to classify the free-text reasons for 28,561 clinical trials that stopped before their endpoints were met. We then evaluated these classes in light of the underlying evidence for the therapeutic hypothesis and target properties. We found that trials are more likely to stop because of a lack of efficacy in the absence of strong genetic evidence from human populations or genetically modified animal models. Furthermore, certain trials are more likely to stop for safety reasons if the drug target gene is highly constrained in human populations and if the gene is broadly expressed across tissues. These results support the growing use of human genetics to evaluate targets for drug discovery programs.
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Affiliation(s)
- Olesya Razuvayevskaya
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Irene Lopez
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Ian Dunham
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - David Ochoa
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
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4
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Bate-Eya LT, Albayrak G, Carr SM, Shrestha A, Kanapin A, Samsonova A, La Thangue NB. Sustained cancer-relevant alternative RNA splicing events driven by PRMT5 in high-risk neuroblastoma. Mol Oncol 2024. [PMID: 39021294 DOI: 10.1002/1878-0261.13702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/12/2024] [Accepted: 07/08/2024] [Indexed: 07/20/2024] Open
Abstract
Protein arginine methyltransferase 5 (PRMT5) is over-expressed in a wide variety of cancers and is implicated as having a key oncogenic role, achieved in part through its control of the master transcription regulator E2F1. We investigated the relevance of PRMT5 and E2F1 in neuroblastoma (NB) and found that elevated expression of PRMT5 and E2F1 occurs in poor prognosis high-risk disease and correlates with an amplified Myelocytomatosis viral-related oncogene, neuroblastoma-derived (MYCN) gene. Our results show that MYCN drives the expression of splicing factor genes that, together with PRMT5 and E2F1, lead to a deregulated alternative RNA splicing programme that impedes apoptosis. Pharmacological inhibition of PRMT5 or inactivation of E2F1 restores normal splicing and renders NB cells sensitive to apoptosis. Our findings suggest that a sustained cancer-relevant alternative RNA splicing programme desensitises NB cells to apoptosis, and identify PRMT5 as a potential therapeutic target for high-risk disease.
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Affiliation(s)
| | - Gulsah Albayrak
- Laboratory of Cancer Biology, Department of Oncology, University of Oxford, UK
| | - Simon Mark Carr
- Laboratory of Cancer Biology, Department of Oncology, University of Oxford, UK
| | - Amit Shrestha
- Laboratory of Cancer Biology, Department of Oncology, University of Oxford, UK
| | - Alexander Kanapin
- Institute of Translational Biomedicine, Saint Petersburg State University, Russia
| | - Anastasia Samsonova
- Institute of Translational Biomedicine, Saint Petersburg State University, Russia
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5
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Reyes Ruiz A, Bhale AS, Venkataraman K, Dimitrov JD, Lacroix-Desmazes S. Binding Promiscuity of Therapeutic Factor VIII. Thromb Haemost 2024. [PMID: 38950594 DOI: 10.1055/a-2358-0853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
The binding promiscuity of proteins defines their ability to indiscriminately bind multiple unrelated molecules. Binding promiscuity is implicated, at least in part, in the off-target reactivity, nonspecific biodistribution, immunogenicity, and/or short half-life of potentially efficacious protein drugs, thus affecting their clinical use. In this review, we discuss the current evidence for the binding promiscuity of factor VIII (FVIII), a protein used for the treatment of hemophilia A, which displays poor pharmacokinetics, and elevated immunogenicity. We summarize the different canonical and noncanonical interactions that FVIII may establish in the circulation and that could be responsible for its therapeutic liabilities. We also provide information suggesting that the FVIII light chain, and especially its C1 and C2 domains, could play an important role in the binding promiscuity. We believe that the knowledge accumulated over years of FVIII usage could be exploited for the development of strategies to predict protein binding promiscuity and therefore anticipate drug efficacy and toxicity. This would open a mutational space to reduce the binding promiscuity of emerging protein drugs while conserving their therapeutic potency.
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Affiliation(s)
- Alejandra Reyes Ruiz
- Centre de Recherche des Cordeliers, Institut National de la Santé et de la Recherche Médicale, CNRS, Sorbonne Université, Université Paris Cité, Paris, France
| | - Aishwarya S Bhale
- Centre for Bio-Separation Technology (CBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
| | - Krishnan Venkataraman
- Centre for Bio-Separation Technology (CBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
| | - Jordan D Dimitrov
- Centre de Recherche des Cordeliers, Institut National de la Santé et de la Recherche Médicale, CNRS, Sorbonne Université, Université Paris Cité, Paris, France
| | - Sébastien Lacroix-Desmazes
- Centre de Recherche des Cordeliers, Institut National de la Santé et de la Recherche Médicale, CNRS, Sorbonne Université, Université Paris Cité, Paris, France
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6
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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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7
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Zohar K, Linial M. Knockdown of DJ-1 Resulted in a Coordinated Activation of the Innate Immune Antiviral Response in HEK293 Cell Line. Int J Mol Sci 2024; 25:7550. [PMID: 39062793 PMCID: PMC11277157 DOI: 10.3390/ijms25147550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
PARK7, also known as DJ-1, plays a critical role in protecting cells by functioning as a sensitive oxidation sensor and modulator of antioxidants. DJ-1 acts to maintain mitochondrial function and regulate transcription in response to different stressors. In this study, we showed that cell lines vary based on their antioxidation potential under basal conditions. The transcriptome of HEK293 cells was tested following knockdown (KD) of DJ-1 using siRNAs, which reduced the DJ-1 transcripts to only 12% of the original level. We compared the expression levels of 14k protein-coding transcripts and 4.2k non-coding RNAs relative to cells treated with non-specific siRNAs. Among the coding genes, approximately 200 upregulated differentially expressed genes (DEGs) signified a coordinated antiviral innate immune response. Most genes were associated with the regulation of type 1 interferons (IFN) and the induction of inflammatory cytokines. About a quarter of these genes were also induced in cells treated with non-specific siRNAs that were used as a negative control. Beyond the antiviral-like response, 114 genes were specific to the KD of DJ-1 with enrichment in RNA metabolism and mitochondrial functions. A smaller set of downregulated genes (58 genes) was associated with dysregulation in membrane structure, cell viability, and mitophagy. We propose that the KD DJ-1 perturbation diminishes the protective potency against oxidative stress. Thus, it renders the cells labile and responsive to the dsRNA signal by activating a large number of genes, many of which drive apoptosis, cell death, and inflammatory signatures. The KD of DJ-1 highlights its potency in regulating genes associated with antiviral responses, RNA metabolism, and mitochondrial functions, apparently through alteration in STAT activity and downstream signaling. Given that DJ-1 also acts as an oncogene in metastatic cancers, targeting DJ-1 could be a promising therapeutic strategy where manipulation of the DJ-1 level may reduce cancer cell viability and enhance the efficacy of cancer treatments.
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Affiliation(s)
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel;
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8
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Siminea N, Czeizler E, Popescu VB, Petre I, Păun A. Connecting the dots: Computational network analysis for disease insight and drug repurposing. Curr Opin Struct Biol 2024; 88:102881. [PMID: 38991238 DOI: 10.1016/j.sbi.2024.102881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/22/2024] [Accepted: 06/19/2024] [Indexed: 07/13/2024]
Abstract
Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.
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Affiliation(s)
- Nicoleta Siminea
- Faculty of Mathematics and Computer Science, University of Bucharest, Romania; National Institute of Research and Development for Biological Sciences, Romania
| | - Eugen Czeizler
- Faculty of Medicine, University of Helsinki, Finland; National Institute of Research and Development for Biological Sciences, Romania
| | | | - Ion Petre
- Department of Mathematics and Statistics, University of Turku, Finland; National Institute of Research and Development for Biological Sciences, Romania.
| | - Andrei Păun
- Faculty of Mathematics and Computer Science, University of Bucharest, Romania; National Institute of Research and Development for Biological Sciences, Romania.
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9
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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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10
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Liu J, Cao S, Imbach KJ, Gritsenko MA, Lih TSM, Kyle JE, Yaron-Barir TM, Binder ZA, Li Y, Strunilin I, Wang YT, Tsai CF, Ma W, Chen L, Clark NM, Shinkle A, Naser Al Deen N, Caravan W, Houston A, Simin FA, Wyczalkowski MA, Wang LB, Storrs E, Chen S, Illindala R, Li YD, Jayasinghe RG, Rykunov D, Cottingham SL, Chu RK, Weitz KK, Moore RJ, Sagendorf T, Petyuk VA, Nestor M, Bramer LM, Stratton KG, Schepmoes AA, Couvillion SP, Eder J, Kim YM, Gao Y, Fillmore TL, Zhao R, Monroe ME, Southard-Smith AN, Li YE, Jui-Hsien Lu R, Johnson JL, Wiznerowicz M, Hostetter G, Newton CJ, Ketchum KA, Thangudu RR, Barnholtz-Sloan JS, Wang P, Fenyö D, An E, Thiagarajan M, Robles AI, Mani DR, Smith RD, Porta-Pardo E, Cantley LC, Iavarone A, Chen F, Mesri M, Nasrallah MP, Zhang H, Resnick AC, Chheda MG, Rodland KD, Liu T, Ding L. Multi-scale signaling and tumor evolution in high-grade gliomas. Cancer Cell 2024; 42:1217-1238.e19. [PMID: 38981438 DOI: 10.1016/j.ccell.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/12/2024] [Accepted: 06/10/2024] [Indexed: 07/11/2024]
Abstract
Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.
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Affiliation(s)
- Jingxian Liu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Kathleen J Imbach
- Josep Carreras Leukaemia Research Institute, Badalona, Spain; Universidad Autónoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Tung-Shing M Lih
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Tomer M Yaron-Barir
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
| | - Zev A Binder
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ilya Strunilin
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yi-Ting Wang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Natalie M Clark
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andrew Shinkle
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Nataly Naser Al Deen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Wagma Caravan
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Andrew Houston
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Faria Anjum Simin
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Erik Storrs
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Siqi Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ritvik Illindala
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yuping D Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Reyka G Jayasinghe
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sandra L Cottingham
- Department of Pathology, Spectrum Health and Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Rosalie K Chu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Karl K Weitz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Tyler Sagendorf
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Michael Nestor
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Athena A Schepmoes
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Sneha P Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Josie Eder
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Young-Mo Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Thomas L Fillmore
- Department of Pathology, Spectrum Health and Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Rui Zhao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Austin N Southard-Smith
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yang E Li
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rita Jui-Hsien Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, Poznań, Poland; Poznan University of Medical Sciences, Poznań, Poland
| | | | | | | | | | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology & Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20850, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | | | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - D R Mani
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | | | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Antonio Iavarone
- Department of Neurological Surgery and Department of Biochemistry, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - MacLean P Nasrallah
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adam C Resnick
- Center for Data Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA.
| | - Karin D Rodland
- Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA.
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.
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11
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Reys V, Pons JL, Labesse G. SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks. Nucleic Acids Res 2024; 52:W313-W317. [PMID: 38783158 PMCID: PMC11223867 DOI: 10.1093/nar/gkae398] [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: 02/29/2024] [Revised: 04/17/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-)interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.
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Affiliation(s)
- Victor Reys
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
| | - Jean-Luc Pons
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
| | - Gilles Labesse
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
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12
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Szulc NA, Stefaniak F, Piechota M, Soszyńska A, Piórkowska G, Cappannini A, Bujnicki J, Maniaci C, Pokrzywa W. DEGRONOPEDIA: a web server for proteome-wide inspection of degrons. Nucleic Acids Res 2024; 52:W221-W232. [PMID: 38567734 PMCID: PMC11223883 DOI: 10.1093/nar/gkae238] [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: 02/02/2024] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 07/06/2024] Open
Abstract
E3 ubiquitin ligases recognize substrates through their short linear motifs termed degrons. While degron-signaling has been a subject of extensive study, resources for its systematic screening are limited. To bridge this gap, we developed DEGRONOPEDIA, a web server that searches for degrons and maps them to nearby residues that can undergo ubiquitination and disordered regions, which may act as protein unfolding seeds. Along with an evolutionary assessment of degron conservation, the server also reports on post-translational modifications and mutations that may modulate degron availability. Acknowledging the prevalence of degrons at protein termini, DEGRONOPEDIA incorporates machine learning to assess N-/C-terminal stability, supplemented by simulations of proteolysis to identify degrons in newly formed termini. An experimental validation of a predicted C-terminal destabilizing motif, coupled with the confirmation of a post-proteolytic degron in another case, exemplifies its practical application. DEGRONOPEDIA can be freely accessed at degronopedia.com.
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Affiliation(s)
- Natalia A Szulc
- Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
| | - Małgorzata Piechota
- Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
| | - Anna Soszyńska
- Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
| | - Gabriela Piórkowska
- Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
| | - Andrea Cappannini
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
| | - Chiara Maniaci
- Medical Research Council (MRC) Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK
| | - Wojciech Pokrzywa
- Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str., 02-109 Warsaw, Poland
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13
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Treccarichi S, Calì F, Vinci M, Ragalmuto A, Musumeci A, Federico C, Costanza C, Bottitta M, Greco D, Saccone S, Elia M. Implications of a De Novo Variant in the SOX12 Gene in a Patient with Generalized Epilepsy, Intellectual Disability, and Childhood Emotional Behavioral Disorders. Curr Issues Mol Biol 2024; 46:6407-6422. [PMID: 39057025 PMCID: PMC11276073 DOI: 10.3390/cimb46070383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
SRY-box transcription factor (SOX) genes, a recently discovered gene family, play crucial roles in the regulation of neuronal stem cell proliferation and glial differentiation during nervous system development and neurogenesis. Whole exome sequencing (WES) in patients presenting with generalized epilepsy, intellectual disability, and childhood emotional behavioral disorder, uncovered a de novo variation within SOX12 gene. Notably, this gene has never been associated with neurodevelopmental disorders. No variants in known genes linked with the patient's symptoms have been detected by the WES Trio analysis. To date, any MIM phenotype number associated with intellectual developmental disorder has not been assigned for SOX12. In contrast, both SOX4 and SOX11 genes within the same C group (SoxC) of the Sox gene family have been associated with neurodevelopmental disorders. The variant identified in the patient here described was situated within the critical high-mobility group (HMG) functional site of the SOX12 protein. This domain, in the Sox protein family, is essential for DNA binding and bending, as well as being responsible for transcriptional activation or repression during the early stages of gene expression. Sequence alignment within SoxC (SOX12, SOX4 and SOX11) revealed a high conservation rate of the HMG region. The in silico predictive analysis described this novel variant as likely pathogenic. Furthermore, the mutated protein structure predictions unveiled notable changes with potential deleterious effects on the protein structure. The aim of this study is to establish a correlation between the SOX12 gene and the symptoms diagnosed in the patient.
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Affiliation(s)
- Simone Treccarichi
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
| | - Francesco Calì
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
| | - Mirella Vinci
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
| | - Alda Ragalmuto
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
| | - Antonino Musumeci
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
| | - Concetta Federico
- Department of Biological, Geological and Environmental Sciences, University of Catania, Via Androne 81, 95124 Catania, Italy;
| | - Carola Costanza
- Department of Sciences for Health Promotion and Mother and Child Care “G. D’Alessandro”, University of Palermo, 90128 Palermo, Italy;
| | - Maria Bottitta
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
| | - Donatella Greco
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
| | - Salvatore Saccone
- Department of Biological, Geological and Environmental Sciences, University of Catania, Via Androne 81, 95124 Catania, Italy;
| | - Maurizio Elia
- Oasi Research Institute-IRCCS, 94018 Troina, Italy; (S.T.); (F.C.); (M.V.); (A.R.); (A.M.); (M.B.); (D.G.); (M.E.)
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14
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Waman VP, Ashford P, Lam SD, Sen N, Abbasian M, Woodridge L, Goldtzvik Y, Bordin N, Wu J, Sillitoe I, Orengo CA. Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics. Sci Rep 2024; 14:14208. [PMID: 38902252 PMCID: PMC11190248 DOI: 10.1038/s41598-024-61541-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 05/07/2024] [Indexed: 06/22/2024] Open
Abstract
The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.
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Affiliation(s)
- Vaishali P Waman
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Paul Ashford
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Su Datt Lam
- Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Mahnaz Abbasian
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Laurel Woodridge
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Yonathan Goldtzvik
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Jiaxin Wu
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Christine A Orengo
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.
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15
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [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: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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16
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Kozlowska J, Humphryes-Kirilov N, Pavlovets A, Connolly M, Kuncheva Z, Horner J, Manso AS, Murray C, Fox JC, McCarthy A. Unveiling new genetic insights in rheumatoid arthritis for drug discovery through Taxonomy3 analysis. Sci Rep 2024; 14:14153. [PMID: 38898196 PMCID: PMC11186831 DOI: 10.1038/s41598-024-64970-0] [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: 01/11/2023] [Accepted: 06/14/2024] [Indexed: 06/21/2024] Open
Abstract
Genetic support for a drug target has been shown to increase the probability of success in drug development, with the potential to reduce attrition in the pharmaceutical industry alongside discovering novel therapeutic targets. It is therefore important to maximise the detection of genetic associations that affect disease susceptibility. Conventional statistical methods such as genome-wide association studies (GWAS) only identify some of the genetic contribution to disease, so novel analytical approaches are required to extract additional insights. C4X Discovery has developed Taxonomy3, a unique method for analysing genetic datasets based on mathematics that is novel in drug discovery. When applied to a previously published rheumatoid arthritis GWAS dataset, Taxonomy3 identified many additional novel genetic signals associated with this autoimmune disease. Follow-up studies using tool compounds support the utility of the method in identifying novel biology and tractable drug targets with genetic support for further investigation.
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Affiliation(s)
- Justyna Kozlowska
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK.
| | | | - Anastasia Pavlovets
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
| | - Martin Connolly
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
| | - Zhana Kuncheva
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
| | - Jonathan Horner
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
| | - Ana Sousa Manso
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
| | - Clare Murray
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
| | - J Craig Fox
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
| | - Alun McCarthy
- C4X Discovery Ltd, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK
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17
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Arizmendi-Izazaga A, Navarro-Tito N, Jiménez-Wences H, Evaristo-Priego A, Priego-Hernández VD, Dircio-Maldonado R, Zacapala-Gómez AE, Mendoza-Catalán MÁ, Illades-Aguiar B, De Nova Ocampo MA, Salmerón-Bárcenas EG, Leyva-Vázquez MA, Ortiz-Ortiz J. Bioinformatics Analysis Reveals E6 and E7 of HPV 16 Regulate Metabolic Reprogramming in Cervical Cancer, Head and Neck Cancer, and Colorectal Cancer through the PHD2-VHL-CUL2-ELOC-HIF-1α Axis. Curr Issues Mol Biol 2024; 46:6199-6222. [PMID: 38921041 PMCID: PMC11202971 DOI: 10.3390/cimb46060370] [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: 05/02/2024] [Revised: 06/06/2024] [Accepted: 06/15/2024] [Indexed: 06/27/2024] Open
Abstract
Human papillomavirus 16 (HPV 16) infection is associated with several types of cancer, such as head and neck, cervical, anal, and penile cancer. Its oncogenic potential is due to the ability of the E6 and E7 oncoproteins to promote alterations associated with cell transformation. HPV 16 E6 and E7 oncoproteins increase metabolic reprogramming, one of the hallmarks of cancer, by increasing the stability of hypoxia-induced factor 1 α (HIF-1α) and consequently increasing the expression levels of their target genes. In this report, by bioinformatic analysis, we show the possible effect of HPV 16 oncoproteins E6 and E7 on metabolic reprogramming in cancer through the E6-E7-PHD2-VHL-CUL2-ELOC-HIF-1α axis. We proposed that E6 and E7 interact with VHL, CUL2, and ELOC in forming the E3 ubiquitin ligase complex that ubiquitinates HIF-1α for degradation via the proteasome. Based on the information found in the databases, it is proposed that E6 interacts with VHL by blocking its interaction with HIF-1α. On the other hand, E7 interacts with CUL2 and ELOC, preventing their binding to VHL and RBX1, respectively. Consequently, HIF-1α is stabilized and binds with HIF-1β to form the active HIF1 complex that binds to hypoxia response elements (HREs), allowing the expression of genes related to energy metabolism. In addition, we suggest an effect of E6 and E7 at the level of PHD2, VHL, CUL2, and ELOC gene expression. Here, we propose some miRNAs targeting PHD2, VHL, CUL2, and ELOC mRNAs. The effect of E6 and E7 may be the non-hydroxylation and non-ubiquitination of HIF-1α, which may regulate metabolic processes involved in metabolic reprogramming in cancer upon stabilization, non-degradation, and translocation to the nucleus.
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Affiliation(s)
- Adán Arizmendi-Izazaga
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Napoleón Navarro-Tito
- Laboratorio de Biología Celular del Cáncer, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
| | - Hilda Jiménez-Wences
- Laboratorio de Investigación en Biomoléculas, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
- Laboratorio de Investigación Clínica, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
| | - Adilene Evaristo-Priego
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Víctor Daniel Priego-Hernández
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Roberto Dircio-Maldonado
- Laboratorio de Investigación Clínica, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
| | - Ana Elvira Zacapala-Gómez
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Miguel Ángel Mendoza-Catalán
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Berenice Illades-Aguiar
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Mónica Ascención De Nova Ocampo
- Escuela Nacional de Medicina y Homeopatía, Programa Institucional de Biomedicina Molecular, Instituto Politécnico Nacional, Guillermo Massieu Helguera No. 239 Col. Fracc. La Escalera-Ticomán, Ciudad de Mexico C.P. 07320, Mexico;
| | - Eric Genaro Salmerón-Bárcenas
- Departamento de Biomedicina Molecular, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Ciudad de México C.P. 07360, Mexico;
| | - Marco Antonio Leyva-Vázquez
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Julio Ortiz-Ortiz
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
- Laboratorio de Investigación en Biomoléculas, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
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18
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Yang X, Zhu J, Wang Q, Tang B, Shen Y, Wang B, Ji L, Liu H, Wuchty S, Zhang Z, Dong Y, Liang Z. Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations. mSystems 2024; 9:e0138523. [PMID: 38752789 PMCID: PMC11237560 DOI: 10.1128/msystems.01385-23] [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: 12/20/2023] [Accepted: 04/10/2024] [Indexed: 06/19/2024] Open
Abstract
A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19. IMPORTANCE A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.
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Affiliation(s)
- Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Jialin Zhu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Qingyun Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bo Tang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Ye Shen
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bingjie Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Li Ji
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Huihui Liu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, Florida, USA
- Department of Biology, University of Miami, Miami, Florida, USA
- Institute of Data Science and Computation, University of Miami, Miami, Florida, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, Florida, USA
| | - Ziding Zhang
- College of Biological Sciences, China Agricultural University, Beijing, China
| | - Yujun Dong
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Zeyin Liang
- Department of Hematology, Peking University First Hospital, Beijing, China
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19
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Li H, Liu J, Lai J, Su X, Wang X, Cao J, Mao S, Zhang T, Gu Q. The HHEX-ABI2/SLC17A9 axis induces cancer stem cell-like properties and tumorigenesis in HCC. J Transl Med 2024; 22:537. [PMID: 38844969 PMCID: PMC11155165 DOI: 10.1186/s12967-024-05324-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/20/2024] [Indexed: 06/10/2024] Open
Abstract
Accumulating evidence indicated that HHEX participated in the initiation and development of several cancers, but the potential roles and mechanisms of HHEX in hepatocellular carcinoma (HCC) were largely unclear. Cancer stem cells (CSCs) are responsible for cancer progression owing to their stemness characteristics. We reported that HHEX was a novel CSCs target for HCC. We found that HHEX was overexpressed in HCC tissues and high expression of HHEX was associated with poor survival. Subsequently, we found that HHEX promoted HCC cell proliferation, migration, and invasion. Moreover, bioinformatics analysis and experiments verified that HHEX promoted stem cell-like properties in HCC. Mechanistically, ABI2 serving as a co-activator of transcriptional factor HHEX upregulated SLC17A9 to promote HCC cancer stem cell-like properties and tumorigenesis. Collectively, the HHEX-mediated ABI2/SLC17A9 axis contributes to HCC growth and metastasis by maintaining the CSC population, suggesting that HHEX serves as a promising therapeutic target for HCC treatment.
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Affiliation(s)
- Huizi Li
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
| | - Jin Liu
- Department of Radiology, University of California, San Diego, The, USA
| | - Jie Lai
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Xinyao Su
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Xiaofeng Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiaqing Cao
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Shengxun Mao
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
| | - Tong Zhang
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Actuated Hospital of Sun Yat-sen University, Guangzhou, 510000, Guangdong, China.
- Department of General Surgery, School of Medicine, Organ Transplantation Clinical Medical Center of Xiamen University, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, 361000, China.
- Department of Organ Transplantation, School of Medicine, Organ Transplantation Clinical Medical Center of Xiamen University, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, 361005, Fujian, China.
| | - Qiuping Gu
- Department of Gastroenterology, Ganzhou People's Hospital, No. 16, Meiguan Avenue, Zhanggong District, Ganzhou City, 341000, Jiangxi Province, People's Republic of China.
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20
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Shrestha S, Taujale R, Katiyar S, Kannan N. Multi-omics reveals new links between Fructosamine-3-Kinase (FN3K) and core metabolic pathways. NPJ Syst Biol Appl 2024; 10:64. [PMID: 38830903 PMCID: PMC11148063 DOI: 10.1038/s41540-024-00390-0] [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: 02/06/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered.
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Affiliation(s)
- Safal Shrestha
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Rahil Taujale
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA
| | - Samiksha Katiyar
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
| | - Natarajan Kannan
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA.
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21
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Shen S, Sobczyk MK, Paternoster L, Brown SJ. From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics. J Invest Dermatol 2024; 144:1189-1199.e8. [PMID: 38782533 DOI: 10.1016/j.jid.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 05/25/2024]
Abstract
Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.
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Affiliation(s)
- Silvia Shen
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Institute for Evolution and Ecology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| | - Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sara J Brown
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, NHS Lothian, Edinburgh, United Kingdom
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22
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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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23
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Jänes J, Müller M, Selvaraj S, Manoel D, Stephenson J, Gonçalves C, Lafita A, Polacco B, Obernier K, Alasoo K, Lemos MC, Krogan N, Martin M, Saraiva LR, Burke D, Beltrao P. Predicted mechanistic impacts of human protein missense variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596373. [PMID: 38854010 PMCID: PMC11160786 DOI: 10.1101/2024.05.29.596373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.
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Affiliation(s)
- Jürgen Jänes
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marc Müller
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Senthil Selvaraj
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Diogo Manoel
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - James Stephenson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Catarina Gonçalves
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Benjamin Polacco
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
| | - Kirsten Obernier
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Manuel C. Lemos
- CICS-UBI, Health Sciences Research Centre, University of Beira Interior, 6200-506, Covilhã, Portugal
| | - Nevan Krogan
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Maria Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Luis R. Saraiva
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - David Burke
- Faculty of Life Sciences and Medicine, King’s College, London, UK
| | - Pedro Beltrao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
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24
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Wang L, Sun F, Li Q, Ma H, Zhong J, Zhang H, Cheng S, Wu H, Zhao Y, Wang N, Xie Z, Zhao M, Zhu P, Zheng H. CytoSIP: an annotated structural atlas for interactions involving cytokines or cytokine receptors. Commun Biol 2024; 7:630. [PMID: 38789577 PMCID: PMC11126726 DOI: 10.1038/s42003-024-06289-0] [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: 05/23/2023] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
Therapeutic agents targeting cytokine-cytokine receptor (CK-CKR) interactions lead to the disruption in cellular signaling and are effective in treating many diseases including tumors. However, a lack of universal and quick access to annotated structural surface regions on CK/CKR has limited the progress of a structure-driven approach in developing targeted macromolecular drugs and precision medicine therapeutics. Herein we develop CytoSIP (Single nucleotide polymorphisms (SNPs), Interface, and Phenotype), a rich internet application based on a database of atomic interactions around hotspots in experimentally determined CK/CKR structural complexes. CytoSIP contains: (1) SNPs on CK/CKR; (2) interactions involving CK/CKR domains, including CK/CKR interfaces, oligomeric interfaces, epitopes, or other drug targeting surfaces; and (3) diseases and phenotypes associated with CK/CKR or SNPs. The database framework introduces a unique tri-level SIP data model to bridge genetic variants (atomic level) to disease phenotypes (organism level) using protein structure (complexes) as an underlying framework (molecule level). Customized screening tools are implemented to retrieve relevant CK/CKR subset, which reduces the time and resources needed to interrogate large datasets involving CK/CKR surface hotspots and associated pathologies. CytoSIP portal is publicly accessible at https://CytoSIP.biocloud.top , facilitating the panoramic investigation of the context-dependent crosstalk between CK/CKR and the development of targeted therapeutic agents.
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Affiliation(s)
- Lu Wang
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China
| | - Fang Sun
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
- Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410006, China
| | - Qianying Li
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Haojie Ma
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Juanhong Zhong
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Huihui Zhang
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Siyi Cheng
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China
| | - Hao Wu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China
| | - Yanmin Zhao
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Nasui Wang
- Division of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, 515041, China
| | - Zhongqiu Xie
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
| | - Mingyi Zhao
- Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410006, China.
| | - Ping Zhu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China.
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China.
| | - Heping Zheng
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China.
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25
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Sibai DS, Tremblay MG, Lessard F, Tav C, Sabourin-Félix M, Robinson M, Moss T. TTF1 control of LncRNA synthesis delineates a tumor suppressor pathway directly regulating the ribosomal RNA genes. J Cell Physiol 2024. [PMID: 38764354 DOI: 10.1002/jcp.31303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/21/2024]
Abstract
The tumor suppressor p14/19ARF regulates ribosomal RNA (rRNA) synthesis by controlling the nucleolar localization of Transcription Termination Factor 1 (TTF1). However, the role played by TTF1 in regulating the rRNA genes and in potentially controlling growth has remained unclear. We now show that TTF1 expression regulates cell growth by determining the cellular complement of ribosomes. Unexpectedly, it achieves this by acting as a "roadblock" to synthesis of the noncoding LncRNA and pRNA that we show are generated from the "Spacer Promoter" duplications present upstream of the 47S pre-rRNA promoter on the mouse and human ribosomal RNA genes. Unexpectedly, the endogenous generation of these noncoding RNAs does not induce CpG methylation or gene silencing. Rather, it acts in cis to suppress 47S preinitiation complex formation and hence de novo pre-rRNA synthesis by a mechanism reminiscent of promoter interference or occlusion. Taken together, our data delineate a pathway from p19ARF to cell growth suppression via the regulation of ribosome biogenesis by noncoding RNAs and validate a key cellular growth law in mammalian cells.
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Affiliation(s)
- Dany S Sibai
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec City, Quebec, Canada
- Cancer Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Michel G Tremblay
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Frédéric Lessard
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Christophe Tav
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec City, Quebec, Canada
- Cancer Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Marianne Sabourin-Félix
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Mark Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Tom Moss
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec City, Quebec, Canada
- Cancer Research Centre, Laval University, Quebec City, Quebec, Canada
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26
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Gottumukkala SB, Ganesan TS, Palanisamy A. Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer. NPJ Syst Biol Appl 2024; 10:53. [PMID: 38760412 PMCID: PMC11101644 DOI: 10.1038/s41540-024-00378-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/29/2024] [Indexed: 05/19/2024] Open
Abstract
Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.
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Affiliation(s)
| | - Trivadi Sundaram Ganesan
- Department of Medical Oncology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
| | - Anbumathi Palanisamy
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, India.
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27
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González-Esparragoza D, Carrasco-Carballo A, Rosas-Murrieta NH, Millán-Pérez Peña L, Luna F, Herrera-Camacho I. In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe. Curr Issues Mol Biol 2024; 46:4609-4629. [PMID: 38785548 PMCID: PMC11120530 DOI: 10.3390/cimb46050280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.
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Affiliation(s)
- Dalia González-Esparragoza
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
- Laboratorio de Elucidación y Síntesis en Química Orgánica, Instituto de Ciencias de la Universidad Autónoma de Puebla (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico
| | - Alan Carrasco-Carballo
- Laboratorio de Elucidación y Síntesis en Química Orgánica, Instituto de Ciencias de la Universidad Autónoma de Puebla (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico
- Consejo Nacional de Humanidades Ciencia y Tecnología, Instituto de Ciencias de la Universidad Autónoma de Puebla (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico
| | - Nora H. Rosas-Murrieta
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
| | - Lourdes Millán-Pérez Peña
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
| | - Felix Luna
- Laboratorio de Neuroendocrinología, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico;
| | - Irma Herrera-Camacho
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
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28
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Kazakov AS, Rastrygina VA, Vologzhannikova AA, Zemskova MY, Bobrova LA, Deryusheva EI, Permyakova ME, Sokolov AS, Litus EA, Shevelyova MP, Uversky VN, Permyakov EA, Permyakov SE. Recognition of granulocyte-macrophage colony-stimulating factor by specific S100 proteins. Cell Calcium 2024; 119:102869. [PMID: 38484433 DOI: 10.1016/j.ceca.2024.102869] [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: 12/28/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 04/05/2024]
Abstract
Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a pleiotropic myelopoietic growth factor and proinflammatory cytokine, clinically used for multiple indications and serving as a promising target for treatment of many disorders, including cancer, multiple sclerosis, rheumatoid arthritis, psoriasis, asthma, COVID-19. We have previously shown that dimeric Ca2+-bound forms of S100A6 and S100P proteins, members of the multifunctional S100 protein family, are specific to GM-CSF. To probe selectivity of these interactions, the affinity of recombinant human GM-CSF to dimeric Ca2+-loaded forms of 18 recombinant human S100 proteins was studied by surface plasmon resonance spectroscopy. Of them, only S100A4 protein specifically binds to GM-CSF with equilibrium dissociation constant, Kd, values of 0.3-2 μM, as confirmed by intrinsic fluorescence and chemical crosslinking data. Calcium removal prevents S100A4 binding to GM-CSF, whereas monomerization of S100A4/A6/P proteins disrupts S100A4/A6 interaction with GM-CSF and induces a slight decrease in S100P affinity for GM-CSF. Structural modelling indicates the presence in the GM-CSF molecule of a conserved S100A4/A6/P-binding site, consisting of the residues from its termini, helices I and III, some of which are involved in the interaction with GM-CSF receptors. The predicted involvement of the 'hinge' region and F89 residue of S100P in GM-CSF recognition was confirmed by mutagenesis. Examination of S100A4/A6/P ability to affect GM-CSF signaling showed that S100A4/A6 inhibit GM-CSF-induced suppression of viability of monocytic THP-1 cells. The ability of the S100 proteins to modulate GM-CSF activity is relevant to progression of various neoplasms and other diseases, according to bioinformatics analysis. The direct regulation of GM-CSF signaling by extracellular forms of the S100 proteins should be taken into account in the clinical use of GM-CSF and development of the therapeutic interventions targeting GM-CSF or its receptors.
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Affiliation(s)
- Alexey S Kazakov
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia.
| | - Victoria A Rastrygina
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Alisa A Vologzhannikova
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Marina Y Zemskova
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia; Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, G.K. Skryabin Institute of Biochemistry and Physiology of Microorganisms, pr. Nauki, 5, Pushchino, Moscow Region 142290, Russia
| | - Lolita A Bobrova
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Evgenia I Deryusheva
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia.
| | - Maria E Permyakova
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Andrey S Sokolov
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Ekaterina A Litus
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Marina P Shevelyova
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Vladimir N Uversky
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia; Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - Eugene A Permyakov
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia
| | - Sergei E Permyakov
- Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, Institute for Biological Instrumentation, Institutskaya str., 7, Pushchino, Moscow Region 142290, Russia.
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29
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Stochaj U. Yeast profilin mutants inhibit classical nuclear import and alter the balance between actin and tubulin levels. Biochem Cell Biol 2024; 102:206-212. [PMID: 38048555 DOI: 10.1139/bcb-2023-0223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023] Open
Abstract
Profilin is a small protein that controls actin polymerization in yeast and higher eukaryotes. In addition, profilin has emerged as a multifunctional protein that contributes to other processes in multicellular organisms. This study focuses on profilin (Pfy1) in the budding yeast Saccharomyces cerevisiae. The primary sequences of yeast Pfy1 and its metazoan orthologs diverge vastly. However, structural elements of profilin are conserved among different species. To date, the full spectrum of Pfy1 functions has yet to be defined. The current work explores the possible involvement of yeast profilin in nuclear protein import. To this end, a panel of well-characterized yeast profilin mutants was evaluated. The experiments demonstrate that yeast profilin (i) regulates nuclear protein import, (ii) determines the subcellular localization of essential nuclear transport factors, and (iii) controls the relative abundance of actin and tubulin. Together, these results define yeast profilin as a moonlighting protein that engages in multiple essential cellular activities.
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Affiliation(s)
- Ursula Stochaj
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, QC H3G 1Y6, Canada
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30
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Jia P, Zhang F, Wu C, Li M. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. Brief Bioinform 2024; 25:bbae162. [PMID: 38739759 PMCID: PMC11089422 DOI: 10.1093/bib/bbae162] [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: 01/01/2024] [Revised: 02/17/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
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Affiliation(s)
- Pengzhen Jia
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
- College of Information Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China
| | - Chaojin Wu
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
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31
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Yeyeodu S, Hanafi D, Webb K, Laurie NA, Kimbro KS. Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease. Front Endocrinol (Lausanne) 2024; 14:1286979. [PMID: 38577257 PMCID: PMC10991756 DOI: 10.3389/fendo.2023.1286979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/07/2023] [Indexed: 04/06/2024] Open
Abstract
Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.
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Affiliation(s)
- Susan Yeyeodu
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
- Charles River Discovery Services, Morrisville, NC, United States
| | - Donia Hanafi
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - Kenisha Webb
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
| | - Nikia A. Laurie
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - K. Sean Kimbro
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
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32
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Hukerikar N, Hingorani AD, Asselbergs FW, Finan C, Schmidt AF. Prioritising genetic findings for drug target identification and validation. Atherosclerosis 2024; 390:117462. [PMID: 38325120 DOI: 10.1016/j.atherosclerosis.2024.117462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation.
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Affiliation(s)
- Nikita Hukerikar
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
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33
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Nishida K, Maruyama J, Kaizu K, Takahashi K, Yugi K. Transomics2cytoscape: an automated software for interpretable 2.5-dimensional visualization of trans-omic networks. NPJ Syst Biol Appl 2024; 10:16. [PMID: 38374087 PMCID: PMC10876688 DOI: 10.1038/s41540-024-00342-8] [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: 04/05/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package 'transomics2cytoscape' for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape .
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Affiliation(s)
- Kozo Nishida
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan
| | - Junichi Maruyama
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Kazunari Kaizu
- Center for Biosystems Dynamics Research (BDR), RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Koichi Takahashi
- Center for Biosystems Dynamics Research (BDR), RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan
| | - Katsuyuki Yugi
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan.
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
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34
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Shrestha S, Taujale R, Katiyar S, Kannan N. Illuminating the functions of the understudied Fructosamine-3-kinase (FN3K) using a multi-omics approach reveals new links to lipid, carbon, and co-factor metabolic pathways. RESEARCH SQUARE 2024:rs.3.rs-3934957. [PMID: 38410452 PMCID: PMC10896376 DOI: 10.21203/rs.3.rs-3934957/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the tree of life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3Ks and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. By identifying a potential link between FN3Ks, redox regulation, and NAD-dependent metabolic processes, our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance is altered.
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Affiliation(s)
- Safal Shrestha
- Institute of Bioinformatics; University of Georgia, Athens, GA, USA
| | - Rahil Taujale
- Department of Biochemistry and Molecular Biology; University of Georgia, Athens, GA, USA
| | - Samiksha Katiyar
- Department of Biochemistry and Molecular Biology; University of Georgia, Athens, GA, USA
| | - Natarajan Kannan
- Institute of Bioinformatics; University of Georgia, Athens, GA, USA
- Department of Biochemistry and Molecular Biology; University of Georgia, Athens, GA, USA
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35
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Kang DS, Moriarty A, Wang YJ, Thomas A, Hao J, Unger BA, Klotz R, Ahmmed S, Amzaleg Y, Martin S, Vanapalli S, Xu K, Smith A, Shen K, Yu M. Ectopic Expression of a Truncated Isoform of Hair Keratin 81 in Breast Cancer Alters Biophysical Characteristics to Promote Metastatic Propensity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2300509. [PMID: 37949677 PMCID: PMC10837353 DOI: 10.1002/advs.202300509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/28/2023] [Indexed: 11/12/2023]
Abstract
Keratins are an integral part of cell structure and function. Here, it is shown that ectopic expression of a truncated isoform of keratin 81 (tKRT81) in breast cancer is upregulated in metastatic lesions compared to primary tumors and patient-derived circulating tumor cells, and is associated with more aggressive subtypes. tKRT81 physically interacts with keratin 18 (KRT18) and leads to changes in the cytosolic keratin intermediate filament network and desmosomal plaque formation. These structural changes are associated with a softer, more elastically deformable cancer cell with enhanced adhesion and clustering ability leading to greater in vivo lung metastatic burden. This work describes a novel biomechanical mechanism by which tKRT81 promotes metastasis, highlighting the importance of the biophysical characteristics of tumor cells.
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Affiliation(s)
- Diane S. Kang
- Department of Stem Cell Biology and Regenerative MedicineKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- USC Norris Comprehensive Cancer CenterKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
| | - Aidan Moriarty
- Department of Stem Cell Biology and Regenerative MedicineKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- USC Norris Comprehensive Cancer CenterKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- Department of PharmacologyUniversity of Maryland School of MedicineBaltimoreMD21201USA
- Marlene and Stewart Greenebaum Comprehensive Cancer CenterUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Yiru Jess Wang
- Department of Stem Cell Biology and Regenerative MedicineKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- USC Norris Comprehensive Cancer CenterKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- Department of PharmacologyUniversity of Maryland School of MedicineBaltimoreMD21201USA
- Marlene and Stewart Greenebaum Comprehensive Cancer CenterUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Amal Thomas
- Department of Molecular and Computational BiologyUSC David and Dana Dornsife College of LettersArts and SciencesUniversity of Southern CaliforniaLos AngelesCA90089USA
| | - Jia Hao
- Department of Biomedical EngineeringViterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesCA90089USA
| | - Bret A. Unger
- Department of ChemistryUniversity of California at BerkeleyBerkeleyCA94720USA
| | - Remi Klotz
- Department of Stem Cell Biology and Regenerative MedicineKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- USC Norris Comprehensive Cancer CenterKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- Department of PharmacologyUniversity of Maryland School of MedicineBaltimoreMD21201USA
- Marlene and Stewart Greenebaum Comprehensive Cancer CenterUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Shamim Ahmmed
- Department of Chemical EngineeringTexas Tech UniversityLubbockTX79409USA
| | - Yonatan Amzaleg
- Department of Stem Cell Biology and Regenerative MedicineKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- USC Norris Comprehensive Cancer CenterKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
| | - Stuart Martin
- Department of PharmacologyUniversity of Maryland School of MedicineBaltimoreMD21201USA
- Marlene and Stewart Greenebaum Comprehensive Cancer CenterUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Siva Vanapalli
- Department of Chemical EngineeringTexas Tech UniversityLubbockTX79409USA
| | - Ke Xu
- Department of ChemistryUniversity of California at BerkeleyBerkeleyCA94720USA
| | - Andrew Smith
- Department of Molecular and Computational BiologyUSC David and Dana Dornsife College of LettersArts and SciencesUniversity of Southern CaliforniaLos AngelesCA90089USA
| | - Keyue Shen
- Department of Biomedical EngineeringViterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesCA90089USA
| | - Min Yu
- Department of Stem Cell Biology and Regenerative MedicineKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- USC Norris Comprehensive Cancer CenterKeck School of Medicine of the University of Southern CaliforniaLos AngelesCA90033USA
- Department of PharmacologyUniversity of Maryland School of MedicineBaltimoreMD21201USA
- Marlene and Stewart Greenebaum Comprehensive Cancer CenterUniversity of Maryland School of MedicineBaltimoreMD21201USA
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36
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Amahong K, Zhang W, Liu Y, Li T, Huang S, Han L, Tao L, Zhu F. RVvictor: Virus RNA-directed molecular interactions for RNA virus infection. Comput Biol Med 2024; 169:107886. [PMID: 38157777 DOI: 10.1016/j.compbiomed.2023.107886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.
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Affiliation(s)
- Kuerbannisha Amahong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Teng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Lianyi Han
- Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, 315211, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
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37
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Lee CY, Hubrich D, Varga JK, Schäfer C, Welzel M, Schumbera E, Djokic M, Strom JM, Schönfeld J, Geist JL, Polat F, Gibson TJ, Keller Valsecchi CI, Kumar M, Schueler-Furman O, Luck K. Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation. Mol Syst Biol 2024; 20:75-97. [PMID: 38225382 PMCID: PMC10883280 DOI: 10.1038/s44320-023-00005-6] [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: 08/03/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/17/2024] Open
Abstract
Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.
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Affiliation(s)
- Chop Yan Lee
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Dalmira Hubrich
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Julia K Varga
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, 9112001, Israel
| | | | - Mareen Welzel
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Eric Schumbera
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
- Computational Biology and Data Mining Group Biozentrum I, 55128, Mainz, Germany
| | - Milena Djokic
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Joelle M Strom
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Jonas Schönfeld
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Johanna L Geist
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Feyza Polat
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | | | - Manjeet Kumar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, 9112001, Israel.
| | - Katja Luck
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany.
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38
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Meyniel-Schicklin L, Amaudrut J, Mallinjoud P, Guillier F, Mangeot PE, Lines L, Aublin-Gex A, Scholtes C, Punginelli C, Joly S, Vasseur F, Manet E, Gruffat H, Henry T, Halitim F, Paparin JL, Machin P, Darteil R, Sampson D, Mikaelian I, Lane L, Navratil V, Golinelli-Cohen MP, Terzi F, André P, Lotteau V, Vonderscher J, Meldrum EC, de Chassey B. Viruses traverse the human proteome through peptide interfaces that can be biomimetically leveraged for drug discovery. Proc Natl Acad Sci U S A 2024; 121:e2308776121. [PMID: 38252831 PMCID: PMC10835127 DOI: 10.1073/pnas.2308776121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/06/2023] [Indexed: 01/24/2024] Open
Abstract
We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases.
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Affiliation(s)
| | | | | | | | - Philippe E. Mangeot
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | | | - Anne Aublin-Gex
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Caroline Scholtes
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Claire Punginelli
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | | | - Florence Vasseur
- Université de Paris, INSERM U1151, CNRS UMR 8253, Institut Necker Enfants Malades, Département “Croissance et Signalisation”, Paris75015, France
| | - Evelyne Manet
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Henri Gruffat
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Thomas Henry
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | | | | | | | | | | | - Ivan Mikaelian
- Université de Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de recherche en cancérologie de Lyon, Lyon69373, France
| | - Lydie Lane
- Computer and Laboratory Investigation of Proteins of Human Origin Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Vincent Navratil
- Pôle Rhône-Alpes de bioinformatique, Rhône-Alpes Bioinformatics Center, Université Lyon 1, Villeurbanne69622, France
- European Virus Bio-informatiques Center, Jena07743, Germany
- Institut Français de Bioinformatique, IFB-core, UMS 3601, Évry91057, France
| | - Marie-Pierre Golinelli-Cohen
- Université Paris-Saclay, CNRS, Institut de Chimie des Substances Naturelles, Unité Propre de Recherche 2301, Gif-sur-Yvette91198, France
| | - Fabiola Terzi
- Université de Paris, INSERM U1151, CNRS UMR 8253, Institut Necker Enfants Malades, Département “Croissance et Signalisation”, Paris75015, France
| | - Patrice André
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
| | - Vincent Lotteau
- Centre International de Recherche en Infectiologie, University Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Lyon69007, France
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39
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Yang X, Wuchty S, Liang Z, Ji L, Wang B, Zhu J, Zhang Z, Dong Y. Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM. Brief Bioinform 2024; 25:bbae005. [PMID: 38279649 PMCID: PMC10818167 DOI: 10.1093/bib/bbae005] [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: 11/20/2023] [Revised: 12/25/2023] [Accepted: 01/01/2021] [Indexed: 01/28/2024] Open
Abstract
The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.
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Affiliation(s)
- Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami FL, 33146, USA
- Department of Biology, University of Miami, Miami FL, 33146, USA
- Institute of Data Science and Computation, University of Miami, Miami, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Zeyin Liang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Li Ji
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bingjie Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Jialin Zhu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yujun Dong
- Department of Hematology, Peking University First Hospital, Beijing, China
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40
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Gupta P, Elser J, Hooks E, D’Eustachio P, Jaiswal P, Naithani S. Plant Reactome Knowledgebase: empowering plant pathway exploration and OMICS data analysis. Nucleic Acids Res 2024; 52:D1538-D1547. [PMID: 37986220 PMCID: PMC10767815 DOI: 10.1093/nar/gkad1052] [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: 09/22/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 11/22/2023] Open
Abstract
Plant Reactome (https://plantreactome.gramene.org) is a freely accessible, comprehensive plant pathway knowledgebase. It provides curated reference pathways from rice (Oryza sativa) and gene-orthology-based pathway projections to 129 additional species, spanning single-cell photoautotrophs, non-vascular plants, and higher plants, thus encompassing a wide-ranging taxonomic diversity. Currently, Plant Reactome houses a collection of 339 reference pathways, covering metabolic and transport pathways, hormone signaling, genetic regulations of developmental processes, and intricate transcriptional networks that orchestrate a plant's response to abiotic and biotic stimuli. Beyond being a mere repository, Plant Reactome serves as a dynamic data discovery platform. Users can analyze and visualize omics data, such as gene expression, gene-gene interaction, proteome, and metabolome data, all within the rich context of plant pathways. Plant Reactome is dedicated to fostering data interoperability, upholding global data standards, and embracing the tenets of the Findable, Accessible, Interoperable and Re-usable (FAIR) data policy.
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Affiliation(s)
- Parul Gupta
- Department of Botany & Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Justin Elser
- Department of Botany & Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Elizabeth Hooks
- Department of Botany & Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | | | - Pankaj Jaiswal
- Department of Botany & Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sushma Naithani
- Department of Botany & Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
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41
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Ghafouri H, Lazar T, Del Conte A, Tenorio Ku LG, Tompa P, Tosatto SCE, Monzon AM. PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins. Nucleic Acids Res 2024; 52:D536-D544. [PMID: 37904608 PMCID: PMC10767937 DOI: 10.1093/nar/gkad947] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 11/01/2023] Open
Abstract
The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.
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Affiliation(s)
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Alessio Del Conte
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | - Peter Tompa
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Institute of Enzymology, Research Centre for Natural Sciences (RCNS), Budapest, Hungary
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42
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Lv D, Li D, Cai Y, Guo J, Chu S, Yu J, Liu K, Jiang T, Ding N, Jin X, Li Y, Xu J. CancerProteome: a resource to functionally decipher the proteome landscape in cancer. Nucleic Acids Res 2024; 52:D1155-D1162. [PMID: 37823596 PMCID: PMC10767844 DOI: 10.1093/nar/gkad824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/07/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.
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Affiliation(s)
- Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Donghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Yangyang Cai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiyu Guo
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Sen Chu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiaxin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Kefan Liu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Xiyun Jin
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Province 150000, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
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43
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Mezghrani A, Simon J, Reys V, Labesse G. Detection and Analysis of Short Linear Motif-Based Protein-Protein Interactions with SLiMAn2 Web Server. Methods Mol Biol 2024; 2836:253-281. [PMID: 38995545 DOI: 10.1007/978-1-0716-4007-4_14] [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: 07/13/2024]
Abstract
Interactomics is bringing a deluge of data regarding protein-protein interactions (PPIs) which are involved in various molecular processes in all types of cells. However, this information does not easily translate into direct and precise molecular interfaces. This limits our understanding of each interaction network and prevents their efficient modulation. A lot of the detected interactions involve recognition of short linear motifs (SLiMs) by a folded domain while others rely on domain-domain interactions. Functional SLiMs hide among a lot of spurious ones, making deeper analysis of interactomes tedious. Hence, actual contacts and direct interactions are difficult to identify.Consequently, there is a need for user-friendly bioinformatic tools, enabling rapid molecular and structural analysis of SLiM-based PPIs in a protein network. In this chapter, we describe the use of the new webserver SLiMAn to help digging into SLiM-based PPIs in an interactive fashion.
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Affiliation(s)
- Alexandre Mezghrani
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France
| | - Juliette Simon
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France
| | - Victor Reys
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France.
| | - Gilles Labesse
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France.
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Csabai L, Bohár B, Türei D, Prabhu S, Földvári-Nagy L, Madgwick M, Fazekas D, Módos D, Ölbei M, Halka T, Poletti M, Kornilova P, Kadlecsik T, Demeter A, Szalay-Bekő M, Kapuy O, Lenti K, Vellai T, Gul L, Korcsmáros T. AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation. Autophagy 2024; 20:188-201. [PMID: 37589496 PMCID: PMC10761021 DOI: 10.1080/15548627.2023.2247737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/31/2023] [Accepted: 08/06/2023] [Indexed: 08/18/2023] Open
Abstract
Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.
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Affiliation(s)
- Luca Csabai
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Balázs Bohár
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | | | - László Földvári-Nagy
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Matthew Madgwick
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | - Dávid Fazekas
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dezső Módos
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | - Márton Ölbei
- Earlham Institute, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Themis Halka
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Martina Poletti
- Earlham Institute, Norwich, UK
- Quadram Institute, Norwich Research Park, Norwich, UK
| | | | - Tamás Kadlecsik
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
| | | | | | - Orsolya Kapuy
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Katalin Lenti
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Tibor Vellai
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
- ELKH/MTA-ELTE Genetics Research Group, Budapest, Hungary
| | - Lejla Gul
- Earlham Institute, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Tamás Korcsmáros
- Earlham Institute, Norwich, UK
- Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary
- Quadram Institute, Norwich Research Park, Norwich, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
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45
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Savinov A, Swanson S, Keating AE, Li GW. High-throughput computational discovery of inhibitory protein fragments with AlphaFold. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572389. [PMID: 38187731 PMCID: PMC10769210 DOI: 10.1101/2023.12.19.572389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native-like binding interactions. Recently developed experimental approaches allow for high-throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind their protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full-length protein targets in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. These predictions were compared with experimentally measured peaks of inhibitory activity in E. coli. We establish that our approach is a sensitive predictor of protein fragment function: Evaluating inhibitory fragments derived from known protein-protein interaction interfaces, we found 87% were predicted by FragFold to bind in a native-like mode. Across full protein sequences, 68% of FragFold-predicted binding peaks match experimentally measured inhibitory peaks. This is true even when the underlying inhibitory mechanism is unclear from existing structural data, and we find FragFold is able to predict novel binding modes for inhibitory fragments of unknown structure, explaining previous genetic and biochemical data for these fragments. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.
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Affiliation(s)
- Andrew Savinov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sebastian Swanson
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Amy E. Keating
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Center for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gene-Wei Li
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
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Sanchez-Rodriguez L, Galvez-Fernandez M, Rojas-Benedicto A, Domingo-Relloso A, Amigo N, Redon J, Monleon D, Saez G, Tellez-Plaza M, Martin-Escudero JC, Ramis R. Traffic Density Exposure, Oxidative Stress Biomarkers and Plasma Metabolomics in a Population-Based Sample: The Hortega Study. Antioxidants (Basel) 2023; 12:2122. [PMID: 38136241 PMCID: PMC10740723 DOI: 10.3390/antiox12122122] [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: 11/17/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Exposure to traffic-related air pollution (TRAP) generates oxidative stress, with downstream effects at the metabolic level. Human studies of traffic density and metabolomic markers, however, are rare. The main objective of this study was to evaluate the cross-sectional association between traffic density in the street of residence with oxidative stress and metabolomic profiles measured in a population-based sample from Spain. We also explored in silico the potential biological implications of the findings. Secondarily, we assessed the contribution of oxidative stress to the association between exposure to traffic density and variation in plasma metabolite levels. Traffic density was defined as the average daily traffic volume over an entire year within a buffer of 50 m around the participants' residence. Plasma metabolomic profiles and urine oxidative stress biomarkers were measured in samples from 1181 Hortega Study participants by nuclear magnetic resonance spectroscopy and high-performance liquid chromatography, respectively. Traffic density was associated with 7 (out of 49) plasma metabolites, including amino acids, fatty acids, products of bacterial and energy metabolism and fluid balance metabolites. Regarding urine oxidative stress biomarkers, traffic associations were positive for GSSG/GSH% and negative for MDA. A total of 12 KEGG pathways were linked to traffic-related metabolites. In a protein network from genes included in over-represented pathways and 63 redox-related candidate genes, we observed relevant proteins from the glutathione cycle. GSSG/GSH% and MDA accounted for 14.6% and 12.2% of changes in isobutyrate and the CH2CH2CO fatty acid moiety, respectively, which is attributable to traffic exposure. At the population level, exposure to traffic density was associated with specific urine oxidative stress and plasma metabolites. Although our results support a role of oxidative stress as a biological intermediary of traffic-related metabolic alterations, with potential implications for the co-bacterial and lipid metabolism, additional mechanistic and prospective studies are needed to confirm our findings.
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Affiliation(s)
- Laura Sanchez-Rodriguez
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain; (L.S.-R.); (A.D.-R.); (R.R.)
- Joint Research Institute-National School of Health (IMIENS), National Distance Education University, 28029 Madrid, Spain
| | - Marta Galvez-Fernandez
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain; (L.S.-R.); (A.D.-R.); (R.R.)
| | - Ayelén Rojas-Benedicto
- Joint Research Institute-National School of Health (IMIENS), National Distance Education University, 28029 Madrid, Spain
- Department of Communicable Diseases, National Center for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain
- CIBER on Epidemiology and Public Health, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Arce Domingo-Relloso
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain; (L.S.-R.); (A.D.-R.); (R.R.)
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Nuria Amigo
- Biosfer Teslab, 43201 Reus, Spain;
- Department of Basic Medical Sciences, Universidad de Rovira i Virgili, 43007 Tarragona, Spain
| | - Josep Redon
- Institute for Biomedical Research, Hospital Clinic de Valencia (INCLIVA), 46010 Valencia, Spain
| | - Daniel Monleon
- Institute for Biomedical Research, Hospital Clinic de Valencia (INCLIVA), 46010 Valencia, Spain
| | - Guillermo Saez
- Department of Biochemistry and Molecular Biology, Faculty of Medicine and Dentistry, Clinical Analysis Service, Hospital Universitario Dr. Peset-FISABIO, Universitat de Valencia, 46020 Valencia, Spain;
| | - Maria Tellez-Plaza
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain; (L.S.-R.); (A.D.-R.); (R.R.)
| | - Juan Carlos Martin-Escudero
- Department of Internal Medicine, Hospital Universitario Rio Hortega, University of Valladolid, 47012 Valladolid, Spain;
| | - Rebeca Ramis
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain; (L.S.-R.); (A.D.-R.); (R.R.)
- CIBER on Epidemiology and Public Health, Instituto de Salud Carlos III, 28029 Madrid, Spain
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47
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Manousakis E, Miralles CM, Esquerda MG, Wright RHG. CDKN1A/p21 in Breast Cancer: Part of the Problem, or Part of the Solution? Int J Mol Sci 2023; 24:17488. [PMID: 38139316 PMCID: PMC10743848 DOI: 10.3390/ijms242417488] [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: 10/18/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
Cyclin-dependent kinase inhibitor 1A (Cip1/Waf1/CDKN1A/p21) is a well-established protein, primarily recognised for its pivotal role in the cell cycle, where it induces cell cycle arrest by inhibiting the activity of cyclin-dependent kinases (CDKs). Over the years, extensive research has shed light on various additional mechanisms involving CDKN1A/p21, implicating it in processes such as apoptosis, DNA damage response (DDR), and the regulation of stem cell fate. Interestingly, p21 can function either as an oncogene or as a tumour suppressor in these contexts. Complicating matters further, the expression of CDKN1A/p21 is elevated in certain tumour types while downregulated in others. In this comprehensive review, we provide an overview of the multifaceted functions of CDKN1A/p21, present clinical data pertaining to cancer patients, and delve into potential strategies for targeting CDKN1A/p21 as a therapeutic approach to cancer. Manipulating CDKN1A/p21 shows great promise for therapy given its involvement in multiple cancer hallmarks, such as sustained cell proliferation, the renewal of cancer stem cells (CSCs), epithelial-mesenchymal transition (EMT), cell migration, and resistance to chemotherapy. Given the dual role of CDKN1A/p21 in these processes, a more in-depth understanding of its specific mechanisms of action and its regulatory network is imperative to establishing successful therapeutic interventions.
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Affiliation(s)
| | | | | | - Roni H. G. Wright
- Basic Sciences Department, Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya, 08195 Barcelona, Spain
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48
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Stefanucci L, Collins J, Sims MC, Barrio-Hernandez I, Sun L, Burren OS, Perfetto L, Bender I, Callahan TJ, Fleming K, Guerrero JA, Hermjakob H, Martin MJ, Stephenson J, Paneerselvam K, Petrovski S, Porras P, Robinson PN, Wang Q, Watkins X, Frontini M, Laskowski RA, Beltrao P, Di Angelantonio E, Gomez K, Laffan M, Ouwehand WH, Mumford AD, Freson K, Carss K, Downes K, Gleadall N, Megy K, Bruford E, Vuckovic D. The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants. Blood 2023; 142:2055-2068. [PMID: 37647632 PMCID: PMC10733830 DOI: 10.1182/blood.2023020118] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.
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Affiliation(s)
- Luca Stefanucci
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Janine Collins
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Barts Health NHS Trust, London, United Kingdom
| | - Matthew C. Sims
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, United Kingdom
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom
| | - Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Luanluan Sun
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Oliver S. Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Livia Perfetto
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
- Department of Biology and Biotechnology “C.Darwin,” Sapienza University of Rome, Rome, Italy
| | - Isobel Bender
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Tiffany J. Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
| | - Kathryn Fleming
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
| | - Jose A. Guerrero
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Barts Health NHS Trust, London, United Kingdom
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Maria J. Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - James Stephenson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - NIHR BioResource
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Barts Health NHS Trust, London, United Kingdom
- Department of Haematology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, United Kingdom
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
- Department of Biology and Biotechnology “C.Darwin,” Sapienza University of Rome, Rome, Italy
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
- Centre for Genomics Research, Discovery Sciences, AstraZeneca, Cambridge, United Kingdom
- Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
- Genomic Medicine, The Jackson Laboratory, Farmington, CT
- Institute for Systems Genomics, University of Connecticut, Farmington, CT
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences RILD Building, University of Exeter Medical School, Exeter, United Kingdom
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Health Data Science Centre, Human Technopole, Milan, Italy
- Haemophilia Centre and Thrombosis Unit, Royal Free London NHS Foundation Trust, London, United Kingdom
- Department of Haematology, Imperial College Healthcare NHS Trust, London, United Kingdom
- Department of Immunology and Inflammation, Centre for Haematology, Imperial College London, London, United Kingdom
- Department of Haematology, University College London Hospitals NHS Trust, London, United Kingdom
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Leuven, Belgium
- Cambridge Genomics Laboratory, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Kalpana Paneerselvam
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, AstraZeneca, Cambridge, United Kingdom
- Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Peter N. Robinson
- Genomic Medicine, The Jackson Laboratory, Farmington, CT
- Institute for Systems Genomics, University of Connecticut, Farmington, CT
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Xavier Watkins
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Mattia Frontini
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences RILD Building, University of Exeter Medical School, Exeter, United Kingdom
| | - Roman A. Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Pedro Beltrao
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Emanuele Di Angelantonio
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Health Data Science Centre, Human Technopole, Milan, Italy
| | - Keith Gomez
- Haemophilia Centre and Thrombosis Unit, Royal Free London NHS Foundation Trust, London, United Kingdom
| | - Mike Laffan
- Department of Haematology, Imperial College Healthcare NHS Trust, London, United Kingdom
- Department of Immunology and Inflammation, Centre for Haematology, Imperial College London, London, United Kingdom
| | - Willem H. Ouwehand
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, University College London Hospitals NHS Trust, London, United Kingdom
| | - Andrew D. Mumford
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
| | - Kathleen Freson
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Leuven, Belgium
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge Genomics Laboratory, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Nick Gleadall
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Karyn Megy
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elspeth Bruford
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Dragana Vuckovic
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
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Park J, Son A, Kim H. A protein-protein interaction analysis tool for targeted cross-linking mass spectrometry. Sci Rep 2023; 13:22103. [PMID: 38092875 PMCID: PMC10719354 DOI: 10.1038/s41598-023-49663-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023] Open
Abstract
Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein-protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand. Although the cross-linking mass spectrometry (XL-MS) method is a representative approach to identify physical interactions between proteins, calculating theoretical mass values for application to targeted mass spectrometry can be difficult. To address this challenge, our research team developed PPIAT, a web application that integrates information on reviewed human proteins, protein-protein interactions, cross-linkers, enzymes, and modifications. PPIAT leverages publicly accessible databases such as STRING to identify interactomes associated with target proteins. Moreover, it autonomously computes the theoretical mass value, accounting for all potential cross-linking scenarios pertinent to the application of XL-MS in SRM analysis. The outputs generated by PPIAT can be concisely represented in terms of protein interaction probabilities, complemented by findings from alternative analytical tools like Prego. These comprehensive summaries enable researchers to customize the results according to specific experimental conditions. All functions of PPIAT are available for free on the web application, making it a valuable tool for researchers studying protein-protein interactions.
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Affiliation(s)
- Jongham Park
- Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea
| | - Ahrum Son
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, 92037, USA
| | - Hyunsoo Kim
- Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea.
- Department of Convergent Bioscience and Informatics, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea.
- SCICS, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea.
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Haqqani AS, Mianoor Z, Star AT, Detcheverry FE, Delaney CE, Stanimirovic DB, Hamel E, Badhwar A. Proteome Profiling of Brain Vessels in a Mouse Model of Cerebrovascular Pathology. BIOLOGY 2023; 12:1500. [PMID: 38132326 PMCID: PMC10740654 DOI: 10.3390/biology12121500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023]
Abstract
Cerebrovascular pathology that involves altered protein levels (or signaling) of the transforming growth factor beta (TGFβ) family has been associated with various forms of age-related dementias, including Alzheimer disease (AD) and vascular cognitive impairment and dementia (VCID). Transgenic mice overexpressing TGFβ1 in the brain (TGF mice) recapitulate VCID-associated cerebrovascular pathology and develop cognitive deficits in old age or when submitted to comorbid cardiovascular risk factors for dementia. We characterized the cerebrovascular proteome of TGF mice using mass spectrometry (MS)-based quantitative proteomics. Cerebral arteries were surgically removed from 6-month-old-TGF and wild-type mice, and proteins were extracted and analyzed by gel-free nanoLC-MS/MS. We identified 3602 proteins in brain vessels, with 20 demonstrating significantly altered levels in TGF mice. For total and/or differentially expressed proteins (p ≤ 0.01, ≥ 2-fold change), using multiple databases, we (a) performed protein characterization, (b) demonstrated the presence of their RNA transcripts in both mouse and human cerebrovascular cells, and (c) demonstrated that several of these proteins were present in human extracellular vesicles (EVs) circulating in blood. Finally, using human plasma, we demonstrated the presence of several of these proteins in plasma and plasma EVs. Dysregulated proteins point to perturbed brain vessel vasomotricity, remodeling, and inflammation. Given that blood-isolated EVs are novel, attractive, and a minimally invasive biomarker discovery platform for age-related dementias, several proteins identified in this study can potentially serve as VCID markers in humans.
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Affiliation(s)
- Arsalan S. Haqqani
- Human Health Therapeutics Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada; (A.S.H.); (A.T.S.); (C.E.D.); (D.B.S.)
| | - Zainab Mianoor
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Laboratory, 4545 Chemin Queen Mary, Montreal, QC H3W 1W4, Canada; (Z.M.); (F.E.D.)
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie (CRIUGM), 4545 Chemin Queen Mary, Montreal, QC H3W 1W4, Canada
| | - Alexandra T. Star
- Human Health Therapeutics Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada; (A.S.H.); (A.T.S.); (C.E.D.); (D.B.S.)
| | - Flavie E. Detcheverry
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Laboratory, 4545 Chemin Queen Mary, Montreal, QC H3W 1W4, Canada; (Z.M.); (F.E.D.)
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie (CRIUGM), 4545 Chemin Queen Mary, Montreal, QC H3W 1W4, Canada
| | - Christie E. Delaney
- Human Health Therapeutics Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada; (A.S.H.); (A.T.S.); (C.E.D.); (D.B.S.)
| | - Danica B. Stanimirovic
- Human Health Therapeutics Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada; (A.S.H.); (A.T.S.); (C.E.D.); (D.B.S.)
| | - Edith Hamel
- Laboratory of Cerebrovascular Research, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada;
| | - AmanPreet Badhwar
- Human Health Therapeutics Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada; (A.S.H.); (A.T.S.); (C.E.D.); (D.B.S.)
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Laboratory, 4545 Chemin Queen Mary, Montreal, QC H3W 1W4, Canada; (Z.M.); (F.E.D.)
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie (CRIUGM), 4545 Chemin Queen Mary, Montreal, QC H3W 1W4, Canada
- Laboratory of Cerebrovascular Research, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada;
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