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Niu R, Guo Y, Shang X. GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network. iScience 2024; 27:109387. [PMID: 38510118 PMCID: PMC10951990 DOI: 10.1016/j.isci.2024.109387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
Identifying cancer genes is vital for cancer diagnosis and treatment. However, because of the complexity of cancer occurrence and limited cancer genes knowledge, it is hard to identify cancer genes accurately using only a few omics data, and the overall performance of existing methods is being called for further improvement. Here, we introduce a two-stage gradual-learning strategy GLIMS to predict cancer genes using integrative features from multi-omics data. Firstly, it uses a semi-supervised hierarchical graph neural network to predict the initial candidate cancer genes by integrating multi-omics data and protein-protein interaction (PPI) network. Then, it uses an unsupervised approach to further optimize the initial prediction by integrating the co-splicing network in post-transcriptional regulation, which plays an important role in cancer development. Systematic experiments on multi-omics cancer data demonstrated that GLIMS outperforms the state-of-the-art methods for the identification of cancer genes and it could be a useful tool to help advance cancer analysis.
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Affiliation(s)
- Rui Niu
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yang Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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2
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Fu Y, Zhou Y, Wang K, Li Z, Kong W. Extracellular Matrix Interactome in Modulating Vascular Homeostasis and Remodeling. Circ Res 2024; 134:931-949. [PMID: 38547250 DOI: 10.1161/circresaha.123.324055] [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] [Indexed: 04/02/2024]
Abstract
The ECM (extracellular matrix) is a major component of the vascular microenvironment that modulates vascular homeostasis. ECM proteins include collagens, elastin, noncollagen glycoproteins, and proteoglycans/glycosaminoglycans. ECM proteins form complex matrix structures, such as the basal lamina and collagen and elastin fibers, through direct interactions or lysyl oxidase-mediated cross-linking. Moreover, ECM proteins directly interact with cell surface receptors or extracellular secreted molecules, exerting matricellular and matricrine modulation, respectively. In addition, extracellular proteases degrade or cleave matrix proteins, thereby contributing to ECM turnover. These interactions constitute the ECM interactome network, which is essential for maintaining vascular homeostasis and preventing pathological vascular remodeling. The current review mainly focuses on endogenous matrix proteins in blood vessels and discusses the interaction of these matrix proteins with other ECM proteins, cell surface receptors, cytokines, complement and coagulation factors, and their potential roles in maintaining vascular homeostasis and preventing pathological remodeling.
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Affiliation(s)
- Yi Fu
- Department of Physiology and Pathophysiology (Y.F., K.W., Z.L., W.K.), School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
| | - Yuan Zhou
- Department of Biomedical Informatics (Y.Z.), School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
| | - Kai Wang
- Department of Physiology and Pathophysiology (Y.F., K.W., Z.L., W.K.), School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
| | - Zhuofan Li
- Department of Physiology and Pathophysiology (Y.F., K.W., Z.L., W.K.), School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
| | - Wei Kong
- Department of Physiology and Pathophysiology (Y.F., K.W., Z.L., W.K.), School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
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3
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Ritmeester-Loy SA, Draper IH, Bueter EC, Lautz JD, Zhang-Wong Y, Gustafson JA, Wilson AL, Lin C, Gafken PR, Jensen MC, Orentas R, Smith SEP. Differential protein-protein interactions underlie signaling mediated by the TCR and a 4-1BB domain-containing CAR. Sci Signal 2024; 17:eadd4671. [PMID: 38442200 PMCID: PMC10986860 DOI: 10.1126/scisignal.add4671] [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: 06/13/2022] [Accepted: 01/09/2024] [Indexed: 03/07/2024]
Abstract
Cells rely on activity-dependent protein-protein interactions to convey biological signals. For chimeric antigen receptor (CAR) T cells containing a 4-1BB costimulatory domain, receptor engagement is thought to stimulate the formation of protein complexes similar to those stimulated by T cell receptor (TCR)-mediated signaling, but the number and type of protein interaction-mediating binding domains differ between CARs and TCRs. Here, we performed coimmunoprecipitation mass spectrometry analysis of a second-generation, CD19-directed 4-1BB:ζ CAR (referred to as bbζCAR) and identified 128 proteins that increased their coassociation after target engagement. We compared activity-induced TCR and CAR signalosomes by quantitative multiplex coimmunoprecipitation and showed that bbζCAR engagement led to the activation of two modules of protein interactions, one similar to TCR signaling that was more weakly engaged by bbζCAR as compared with the TCR and one composed of TRAF signaling complexes that was not engaged by the TCR. Batch-to-batch and interindividual variations in production of the cytokine IL-2 correlated with differences in the magnitude of protein network activation. Future CAR T cell manufacturing protocols could measure, and eventually control, biological variation by monitoring these signalosome activation markers.
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Affiliation(s)
- Samuel A. Ritmeester-Loy
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Isabella H. Draper
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Eric C. Bueter
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Jonathan D Lautz
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Yue Zhang-Wong
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Joshua A. Gustafson
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
- Seattle Children’s Therapeutics, Seattle Children’s Research Institute, Seattle, WA 98101 USA
| | - Ashley L. Wilson
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
- Seattle Children’s Therapeutics, Seattle Children’s Research Institute, Seattle, WA 98101 USA
| | - Chenwei Lin
- Proteomics and Metabolomics Facility, Fred Hutchinson Cancer Center, Seattle, WA 98101, USA
| | - Philip R. Gafken
- Proteomics and Metabolomics Facility, Fred Hutchinson Cancer Center, Seattle, WA 98101, USA
| | - Michael C. Jensen
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
- Seattle Children’s Therapeutics, Seattle Children’s Research Institute, Seattle, WA 98101 USA
- Department of Pediatrics, University of Washington, Seattle, WA 98101, USA
| | - Rimas Orentas
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
- Department of Pediatrics, University of Washington, Seattle, WA 98101, USA
| | - Stephen E. P. Smith
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
- Department of Pediatrics, University of Washington, Seattle, WA 98101, USA
- Graduate Program in Neuroscience, University of Washington, Seattle, WA 98101, USA
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Tsare EPG, Klapa MI, Moschonas NK. Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis. Hum Genomics 2024; 18:15. [PMID: 38326862 DOI: 10.1186/s40246-023-00565-6] [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: 08/08/2023] [Accepted: 11/12/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. METHODS The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. RESULTS The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. CONCLUSIONS The implemented workflow could be used for other multifactorial diseases.
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Affiliation(s)
- Evridiki-Pandora G Tsare
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece
| | - Maria I Klapa
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
| | - Nicholas K Moschonas
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece.
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
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Peng L, Tan J, Xiong W, Zhang L, Wang Z, Yuan R, Li Z, Chen X. Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data. Comput Biol Med 2023; 163:107137. [PMID: 37364528 DOI: 10.1016/j.compbiomed.2023.107137] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/18/2023] [Accepted: 06/04/2023] [Indexed: 06/28/2023]
Abstract
BACKGROUND Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis. METHODS Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors. RESULTS The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells. CONCLUSIONS The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Wei Xiong
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Zhao Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Ruya Yuan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
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Kiouri DP, Ntallis C, Kelaidonis K, Peana M, Tsiodras S, Mavromoustakos T, Giuliani A, Ridgway H, Moore GJ, Matsoukas JM, Chasapis CT. Network-Based Prediction of Side Effects of Repurposed Antihypertensive Sartans against COVID-19 via Proteome and Drug-Target Interactomes. Proteomes 2023; 11:21. [PMID: 37368467 DOI: 10.3390/proteomes11020021] [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: 04/10/2023] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 06/28/2023] Open
Abstract
The potential of targeting the Renin-Angiotensin-Aldosterone System (RAAS) as a treatment for the coronavirus disease 2019 (COVID-19) is currently under investigation. One way to combat this disease involves the repurposing of angiotensin receptor blockers (ARBs), which are antihypertensive drugs, because they bind to angiotensin-converting enzyme 2 (ACE2), which in turn interacts with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. However, there has been no in silico analysis of the potential toxicity risks associated with the use of these drugs for the treatment of COVID-19. To address this, a network-based bioinformatics methodology was used to investigate the potential side effects of known Food and Drug Administration (FDA)-approved antihypertensive drugs, Sartans. This involved identifying the human proteins targeted by these drugs, their first neighbors, and any drugs that bind to them using publicly available experimentally supported data, and subsequently constructing proteomes and protein-drug interactomes. This methodology was also applied to Pfizer's Paxlovid, an antiviral drug approved by the FDA for emergency use in mild-to-moderate COVID-19 treatment. The study compares the results for both drug categories and examines the potential for off-target effects, undesirable involvement in various biological processes and diseases, possible drug interactions, and the potential reduction in drug efficiency resulting from proteoform identification.
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Affiliation(s)
- Despoina P Kiouri
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
- Department of Chemistry, Laboratory of Organic Chemistry, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Charalampos Ntallis
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
| | | | - Massimiliano Peana
- Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy
| | - Sotirios Tsiodras
- 4th Department of Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Thomas Mavromoustakos
- Department of Chemistry, Laboratory of Organic Chemistry, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Harry Ridgway
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia
- AquaMem Consultants, Rodeo, NM 88056, USA
| | - Graham J Moore
- Pepmetics Inc., 772 Murphy Place, Victoria, BC V6Y 3H4, Canada
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - John M Matsoukas
- NewDrug PC, Patras Science Park, 26504 Patras, Greece
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Institute for Health and Sport, Victoria University, Melbourne, VIC 3030, Australia
- Department of Chemistry, University of Patras, 26504 Patras, Greece
| | - Christos T Chasapis
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
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Sarafidou T, Galliopoulou E, Apostolopoulou D, Fragkiadakis GA, Moschonas NK. Reconstruction of a Comprehensive Interactome and Experimental Data Analysis of FRA10AC1 May Provide Insights into Its Biological Role in Health and Disease. Genes (Basel) 2023; 14:genes14030568. [PMID: 36980839 PMCID: PMC10048706 DOI: 10.3390/genes14030568] [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: 12/30/2022] [Revised: 02/14/2023] [Accepted: 02/21/2023] [Indexed: 03/03/2023] Open
Abstract
FRA10AC1, the causative gene for the manifestation of the FRA10A fragile site, encodes a well-conserved nuclear protein characterized as a non-core spliceosomal component. Pre-mRNA splicing perturbations have been linked with neurodevelopmental diseases. FRA10AC1 variants have been, recently, causally linked with severe neuropathological and growth retardation phenotypes. To further elucidate the participation of FRA10AC1 in spliceosomal multiprotein complexes and its involvement in neurological phenotypes related to splicing, we exploited protein–protein interaction experimental data and explored network information and information deduced from transcriptomics. We confirmed the direct interaction of FRA10AC1with ESS2, a non-core spliceosomal protein, mapped their interacting domains, and documented their tissue co-localization and physical interaction at the level of intracellular protein stoichiometries. Although FRA10AC1 and SF3B2, a major core spliceosomal protein, were shown to interact under in vitro conditions, the endogenous proteins failed to co-immunoprecipitate. A reconstruction of a comprehensive, strictly binary, protein–protein interaction network of FRA10AC1 revealed dense interconnectivity with many disease-associated spliceosomal components and several non-spliceosomal regulatory proteins. The topological neighborhood of FRA10AC1 depicts an interactome associated with multiple severe monogenic and multifactorial neurodevelopmental diseases mainly referring to spliceosomopathies. Our results suggest that FRA10AC1 involvement in pre-mRNA processing might be strengthened by interconnecting splicing with transcription and mRNA export, and they propose the broader role(s) of FRA10AC1 in cell pathophysiology.
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Affiliation(s)
- Theologia Sarafidou
- Department of Biochemistry and Biotechnology, University of Thessaly, Viopolis, 41500 Larissa, Greece
- Correspondence: (T.S.); (N.K.M.)
| | - Eleni Galliopoulou
- Department of Biochemistry and Biotechnology, University of Thessaly, Viopolis, 41500 Larissa, Greece
| | | | - Georgios A. Fragkiadakis
- Department of Nutrition and Dietetics Sciences, Hellenic Mediterranean University, Tripitos, 72300 Siteia, Greece
| | - Nicholas K. Moschonas
- School of Medicine, University of Patras, 26500 Patras, Greece
- Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH/ICE-HT), 26504 Patras, Greece
- Correspondence: (T.S.); (N.K.M.)
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Antonatos C, Patsatsi A, Zafiriou E, Stavrou EF, Liaropoulos A, Kyriakoy A, Evangelou E, Digka D, Roussaki-Schulze A, Sotiriadis D, Georgiou S, Grafanaki K, Moschonas NΚ, Vasilopoulos Y. Protein network and pathway analysis in a pharmacogenetic study of cyclosporine treatment response in Greek patients with psoriasis. THE PHARMACOGENOMICS JOURNAL 2023; 23:8-13. [PMID: 36229649 DOI: 10.1038/s41397-022-00291-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/15/2022] [Accepted: 09/22/2022] [Indexed: 02/15/2023]
Abstract
Although cyclosporine comprises a well-established systemic therapy for psoriasis, patients show important heterogeneity in their treatment response. The aim of our study was the pharmacogenetic analysis of 200 Greek patients with psoriasis based on the cyclosporine pathway related protein-protein interaction (PPI) network, reconstructed through the PICKLE meta-database. We genotyped 27 single nucleotide polymorphisms, mapped to 22 key protein nodes of the cyclosporine pathway, via the utilization of the iPLEX®GOLD panel of the MassARRAY® System. Single-SNP analyses showed statistically significant associations between CALM1 rs12885713 (P = 0.0108) and MALT1 rs2874116 (P = 0.0006) polymorphisms with positive response to cyclosporine therapy after correction for multiple comparisons, with the haplotype analyses further enhancing the predictive value of rs12885713 as a pharmacogenetic biomarker for cyclosporine therapy (P = 0.0173). Our findings have the potential to improve our prediction of cyclosporine efficacy and safety in psoriasis patients, as well as provide the framework for the pharmacogenetics of biological therapies in complex diseases.
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Affiliation(s)
- Charalabos Antonatos
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, Patras, Greece
| | - Aikaterini Patsatsi
- 2nd Dermatology Department, Medical School, Papageorgiou Hospital, Aristotle University, Thessaloniki, Greece
| | - Efterpi Zafiriou
- Department of Dermatology, University General Hospital Larissa, University of Thessaly, Volos, Greece
| | - Eleana F Stavrou
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, Patras, Greece.,Lab. of General Biology, Medical School, University of Patras, Patras, Greece
| | - Andreas Liaropoulos
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, Patras, Greece
| | - Aikaterini Kyriakoy
- 2nd Dermatology Department, Medical School, Papageorgiou Hospital, Aristotle University, Thessaloniki, Greece
| | - Evangelos Evangelou
- Department of Hygiene and Epidemiology, Medical School, University of Ioannina, Ioannina, Greece.,Department of Epidemiology & Biostatistics, MRC Centre for Environment and Health, Imperial College London, London, UK.,Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Ioannina, Greece
| | - Danai Digka
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, Patras, Greece
| | - Angeliki Roussaki-Schulze
- Department of Dermatology, University General Hospital Larissa, University of Thessaly, Volos, Greece
| | - Dimitris Sotiriadis
- 2nd Dermatology Department, Medical School, Papageorgiou Hospital, Aristotle University, Thessaloniki, Greece
| | - Sophia Georgiou
- Dermatology Department, Medical School, University of Patras, Patras, Greece
| | - Katerina Grafanaki
- Dermatology Department, Medical School, University of Patras, Patras, Greece
| | - Nicholas Κ Moschonas
- Lab. of General Biology, Medical School, University of Patras, Patras, Greece.,Foundation of Research & Technology, Institute of Chemical Engineering Science (ICE-HT), Patras, Greece
| | - Yiannis Vasilopoulos
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, Patras, Greece.
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MIR retrotransposons link the epigenome and the transcriptome of coding genes in acute myeloid leukemia. Nat Commun 2022; 13:6524. [PMID: 36316347 PMCID: PMC9622910 DOI: 10.1038/s41467-022-34211-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
DNMT3A and IDH1/2 mutations combinatorically regulate the transcriptome and the epigenome in acute myeloid leukemia; yet the mechanisms of this interplay are unknown. Using a systems approach within topologically associating domains, we find that genes with significant expression-methylation correlations are enriched in signaling and metabolic pathways. The common denominator across these methylation-regulated genes is the density in MIR retrotransposons of their introns. Moreover, a discrete number of CpGs overlapping enhancers are responsible for regulating most of these genes. Established mouse models recapitulate the dependency of MIR-rich genes on the balanced expression of epigenetic modifiers, while projection of leukemic profiles onto normal hematopoiesis ones further consolidates the dependencies of methylation-regulated genes on MIRs. Collectively, MIR elements on genes and enhancers are susceptible to changes in DNA methylation activity and explain the cooperativity of proteins in this pathway in normal and malignant hematopoiesis.
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10
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How Far Are We from the Completion of the Human Protein Interactome Reconstruction? Biomolecules 2022; 12:biom12010140. [PMID: 35053288 PMCID: PMC8774112 DOI: 10.3390/biom12010140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/09/2022] [Accepted: 01/11/2022] [Indexed: 12/12/2022] Open
Abstract
After more than fifteen years from the first high-throughput experiments for human protein–protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights gained from the holistic investigation of the current network are valid and useful. The unique structure of PICKLE, a meta-database of the human experimentally determined direct PPI network developed by our group, presently covering ~80% of the UniProtKB/Swiss-Prot reviewed human complete proteome, enables the evaluation of the interactome expansion by comparing the successive PICKLE releases since 2013. We observe a gradual overall increase of 39%, 182%, and 67% in protein nodes, PPIs, and supporting references, respectively. Our results indicate that, in recent years, (a) the PPI addition rate has decreased, (b) the new PPIs are largely determined by high-throughput experiments and mainly concern existing protein nodes and (c), as we had predicted earlier, most of the newly added protein nodes have a low degree. These observations, combined with a largely overlapping k-core between PICKLE releases and a network density increase, imply that an almost complete picture of a structurally defined network has been reached. The comparative unsupervised application of two clustering algorithms indicated that exploring the full interactome topology can reveal the protein neighborhoods involved in closely related biological processes as transcriptional regulation, cell signaling and multiprotein complexes such as the connexon complex associated with cancers. A well-reconstructed human protein interactome is a powerful tool in network biology and medicine research forming the basis for multi-omic and dynamic analyses.
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11
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Zhou Y, Cui Q, Zhou Y. Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments. Front Genet 2022; 12:747931. [PMID: 35095997 PMCID: PMC8795687 DOI: 10.3389/fgene.2021.747931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/29/2021] [Indexed: 12/12/2022] Open
Abstract
tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.
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Affiliation(s)
- Yiran Zhou
- MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
- MOE Key Lab of Cardiovascular Sciences, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Qinghua Cui
- MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
- MOE Key Lab of Cardiovascular Sciences, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Yuan Zhou
- MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
- MOE Key Lab of Cardiovascular Sciences, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
- *Correspondence: Yuan Zhou,
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Ringwald M, Richardson JE, Baldarelli RM, Blake JA, Kadin JA, Smith C, Bult CJ. Mouse Genome Informatics (MGI): latest news from MGD and GXD. Mamm Genome 2021; 33:4-18. [PMID: 34698891 PMCID: PMC8913530 DOI: 10.1007/s00335-021-09921-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/21/2021] [Indexed: 12/01/2022]
Abstract
The Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation standards. MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease. MGI is the authoritative source for mouse gene, allele, and strain nomenclature and is the primary source of mouse phenotype annotations, functional annotations, developmental gene expression information, and annotations of mouse models with human diseases. MGI maintains mouse anatomy and phenotype ontologies and contributes to the development of the Gene Ontology and Disease Ontology and uses these ontologies as standard terminologies for annotation. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are MGI's two major knowledgebases. Here, we highlight some of the recent changes and enhancements to MGD and GXD that have been implemented in response to changing needs of the biomedical research community and to improve the efficiency of expert curation. MGI can be accessed freely at http://www.informatics.jax.org .
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Shaukat AN, Kaliatsi EG, Skeparnias I, Stathopoulos C. The Dynamic Network of RNP RNase P Subunits. Int J Mol Sci 2021; 22:ijms221910307. [PMID: 34638646 PMCID: PMC8509007 DOI: 10.3390/ijms221910307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 11/17/2022] Open
Abstract
Ribonuclease P (RNase P) is an important ribonucleoprotein (RNP), responsible for the maturation of the 5′ end of precursor tRNAs (pre-tRNAs). In all organisms, the cleavage activity of a single phosphodiester bond adjacent to the first nucleotide of the acceptor stem is indispensable for cell viability and lies within an essential catalytic RNA subunit. Although RNase P is a ribozyme, its kinetic efficiency in vivo, as well as its structural variability and complexity throughout evolution, requires the presence of one protein subunit in bacteria to several protein partners in archaea and eukaryotes. Moreover, the existence of protein-only RNase P (PRORP) enzymes in several organisms and organelles suggests a more complex evolutionary timeline than previously thought. Recent detailed structures of bacterial, archaeal, human and mitochondrial RNase P complexes suggest that, although apparently dissimilar enzymes, they all recognize pre-tRNAs through conserved interactions. Interestingly, individual protein subunits of the human nuclear and mitochondrial holoenzymes have additional functions and contribute to a dynamic network of elaborate interactions and cellular processes. Herein, we summarize the role of each RNase P subunit with a focus on the human nuclear RNP and its putative role in flawless gene expression in light of recent structural studies.
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Zhou Y, Chen H, Li S, Chen M. mPPI: a database extension to visualize structural interactome in a one-to-many manner. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6307707. [PMID: 34156447 DOI: 10.1093/database/baab036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/10/2021] [Accepted: 05/28/2021] [Indexed: 01/02/2023]
Abstract
Protein-protein interaction (PPI) databases with structural information are useful to investigate biological functions at both systematic and atomic levels. However, most existing PPI databases only curate binary interactome. From the perspective of the display and function of PPI, as well as the structural binding interface, the related database and resources are summarized. We developed a database extension, named mPPI, for PPI structural visualization. Comparing with the existing structural interactomes that curate resolved PPI conformation in pairs, mPPI can visualize target protein and its multiple interactors simultaneously, which facilitates multi-target drug discovery and structure prediction of protein macro-complexes. By employing a protein-protein docking algorithm, mPPI largely extends the coverage of structural interactome from experimentally resolved complexes. mPPI is designed to be a customizable and convenient plugin for PPI databases. It possesses wide potential applications for various PPI databases, and it has been used for a neurodegenerative disease-related PPI database as demonstration. Scripts and implementation guidelines of mPPI are documented at the database tool website. Database URL http://bis.zju.edu.cn/mppi/.
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Affiliation(s)
- Yekai Zhou
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.,Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China
| | - Hongjun Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Sida Li
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.,Bioinformatics Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
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