1
|
Pollet L, Xia Y. Structure-guided Evolutionary Analysis of Interactome Network Rewiring at Single Residue Resolution in Yeasts. J Mol Biol 2024; 436:168641. [PMID: 38844045 DOI: 10.1016/j.jmb.2024.168641] [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: 01/21/2024] [Revised: 04/30/2024] [Accepted: 06/01/2024] [Indexed: 06/16/2024]
Abstract
Protein-protein interactions (PPIs) are known to rewire extensively during evolution leading to lineage-specific and species-specific changes in molecular processes. However, the detailed molecular evolutionary mechanisms underlying interactome network rewiring are not well-understood. Here, we combine high-confidence PPI data, high-resolution three-dimensional structures of protein complexes, and homology-based structural annotation transfer to construct structurally-resolved interactome networks for the two yeasts S. cerevisiae and S. pombe. We then classify PPIs according to whether they are preserved or different between the two yeast species and compare site-specific evolutionary rates of interfacial versus non-interfacial residues for these different categories of PPIs. We find that residues in PPI interfaces evolve significantly more slowly than non-interfacial residues when using lineage-specific measures of evolutionary rate, but not when using non-lineage-specific measures. Furthermore, both lineage-specific and non-lineage-specific evolutionary rate measures can distinguish interfacial residues from non-interfacial residues for preserved PPIs between the two yeasts, but only the lineage-specific measure is appropriate for rewired PPIs. Finally, both lineage-specific and non-lineage-specific evolutionary rate measures are appropriate for elucidating structural determinants of protein evolution for residues outside of PPI interfaces. Overall, our results demonstrate that unlike tertiary structures of single proteins, PPIs and PPI interfaces can be highly volatile in their evolution, thus requiring the use of lineage-specific measures when studying their evolution. These results yield insight into the evolutionary design principles of PPIs and the mechanisms by which interactions are preserved or rewired between species, improving our understanding of the molecular evolution of PPIs and PPI interfaces at the residue level.
Collapse
Affiliation(s)
- Léah Pollet
- Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, QC, Canada
| | - Yu Xia
- Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, QC, Canada.
| |
Collapse
|
2
|
Bandara D, Riccardi K. Graph Node Classification to Predict Autism Risk in Genes. Genes (Basel) 2024; 15:447. [PMID: 38674382 PMCID: PMC11049455 DOI: 10.3390/genes15040447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome band location as node features, and gene interactions as edges. Graph models were employed to classify the autism risk associated with newly introduced genes (test set). Three classification tasks were undertaken to test the ability of our models: binary risk association, multi-class risk association, and syndromic gene association. We tested graph convolutional networks, Graph Sage, graph transformer, and Multi-Layer Perceptron (Baseline) architectures on this problem. The Graph Sage model consistently outperformed the other models, showcasing its utility in classifying ASD-related genes. Our ablation studies show that the chromosome band location and protein interactions contain useful information for this problem. The models achieved 85.80% accuracy on the binary risk classification, 81.68% accuracy on the multi-class risk classification, and 90.22% on the syndromic classification.
Collapse
Affiliation(s)
- Danushka Bandara
- Department of Computer Science and Engineering, Fairfield University, Fairfield, CT 06824, USA;
| | | |
Collapse
|
3
|
Zhang M, Wang J, Wang W, Yang G, Peng J. Predicting cell-type specific disease genes of diabetes with the biological network. Comput Biol Med 2024; 169:107849. [PMID: 38101116 DOI: 10.1016/j.compbiomed.2023.107849] [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: 08/24/2023] [Revised: 11/21/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023]
Abstract
Type 2 diabetes (T2D) is a chronic condition that can lead to significant harm, such as heart disease, kidney disease, nerve damage, and blindness. Although T2D-related genes have been identified through Genome-wide association studies (GWAS) and various computational methods, the biological mechanism of T2D at the cell type level remains unclear. Exploring cell type-specific genes related to T2D is essential to understand the cellular mechanisms underlying the disease. To address this issue, we introduce DiGCellNet (predicting Disease Genes with Cell type specificity based on biological Networks), a model that integrates graph convolutional network (GCN) and multi-task learning (MTL) to predict T2D-associated cell type-specific genes based on the biological network. Our work represents the first attempt to predict cell type-specific disease genes using GCN and MTL. We evaluate our approach by predicting genes specific to four cell types and demonstrate that the proposed DiGCellNet outperforms other models that combine node embeddings with traditional machine learning algorithms. Moreover, DiGCellNet successfully identifies CALM1 as a gene specific to beta cell type in T2D cases, and this association is confirmed using an independent dataset. The code is available at https://github.com/23AIBox/23AIBox-DiGCellNet.
Collapse
Affiliation(s)
- Menghan Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China; The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi'an, 710072, China
| | - Jingru Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China; The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi'an, 710072, China
| | - Wei Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China; The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi'an, 710072, China
| | - Guang Yang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China; The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi'an, 710072, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China; The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi'an, 710072, China; School of Computer Science, Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, 518000, China.
| |
Collapse
|
4
|
Lucena-Padros H, Bravo-Gil N, Tous C, Rojano E, Seoane-Zonjic P, Fernández RM, Ranea JAG, Antiñolo G, Borrego S. Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease. Biomolecules 2024; 14:164. [PMID: 38397401 PMCID: PMC10886964 DOI: 10.3390/biom14020164] [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: 12/05/2023] [Revised: 01/15/2024] [Accepted: 01/27/2024] [Indexed: 02/25/2024] Open
Abstract
Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.
Collapse
Affiliation(s)
- Helena Lucena-Padros
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
| | - Nereida Bravo-Gil
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Cristina Tous
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Elena Rojano
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
| | - Pedro Seoane-Zonjic
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 29071 Malaga, Spain
| | - Raquel María Fernández
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Juan A. G. Ranea
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 29071 Malaga, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
| | - Guillermo Antiñolo
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Salud Borrego
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| |
Collapse
|
5
|
Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
Collapse
Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
6
|
Zhang L, Jin GZ, Li D. Tat-hspb1 Suppresses Clear Cell Renal Cell Carcinoma (ccRCC) Growth via Lysosomal Membrane Permeabilization. Cancers (Basel) 2022; 14:cancers14225710. [PMID: 36428802 PMCID: PMC9688814 DOI: 10.3390/cancers14225710] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer, of which the incidence is increasing worldwide with a high mortality rate. Bioactive peptides are considered a significant class of natural medicines. We applied mass spectrometry-based peptidomic analysis to explore the peptide profile of human renal clear cell carcinoma and adjacent normal tissues. A total of 18,031 peptides were identified, of which 105 unique peptides were differentially expressed (44 were up-regulated and 61 were down-regulated in ccRCC tissues). Through bioinformatic analysis, we finally selected one peptide derived from the HSPB1 protein (amino acids 12-35 of the N-terminal region of HSPB1). Next, we fused this peptide to the HIV-Tat, generated a novel peptide named Tat-hspb1, and found that Tat-hspb1 inhibited ccRCC cells' viability while being less cytotoxic to normal epithelial cells. Furthermore, Tat-hspb1 induced apoptosis and inhibited the proliferation and migration of ccRCC cells. Furthermore, we demonstrated that Tat-hspb1 was predominantly localized in lysosomes after entering the ccRCC cell and induced lysosomal membrane permeabilization (LMP) and the release of cathepsin D from lysosomes. Taken together, Tat-hspb1 has the potential to serve as a new anticancer drug candidate.
Collapse
Affiliation(s)
- Lin Zhang
- Departments of Urology, Tongren Hospital Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
| | - Guang-Zhi Jin
- Hongqiao International Institute of Medicine, Tongren Hospital Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
- Correspondence: (G.-Z.J.); (D.L.)
| | - Dong Li
- Departments of Urology, Tongren Hospital Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
- Correspondence: (G.-Z.J.); (D.L.)
| |
Collapse
|
7
|
Azadifar S, Ahmadi A. A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning. BMC Bioinformatics 2022; 23:422. [PMID: 36241966 PMCID: PMC9563530 DOI: 10.1186/s12859-022-04954-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: 07/12/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022] Open
Abstract
Background Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. There are many machine learning-based gene prioritization methods. These methods differ in various aspects including the feature vectors of genes, the used datasets with different structures, and the learning model. Creating a suitable feature vector for genes and an appropriate learning model on a variety of data with different and non-Euclidean structures, including graphs, as well as the lack of negative data are very important challenges of these methods. The use of graph neural networks has recently emerged in machine learning and other related fields, and they have demonstrated superior performance for a broad range of problems. Methods In this study, a new semi-supervised learning method based on graph convolutional networks is presented using the novel constructing feature vector for each gene. In the proposed method, first, we construct three feature vectors for each gene using terms from the Gene Ontology (GO) database. Then, we train a graph convolution network on these vectors using protein–protein interaction (PPI) network data to identify disease candidate genes. Our model discovers hidden layer representations encoding in both local graph structure as well as features of nodes. This method is characterized by the simultaneous consideration of topological information of the biological network (e.g., PPI) and other sources of evidence. Finally, a validation has been done to demonstrate the efficiency of our method. Results Several experiments are performed on 16 diseases to evaluate the proposed method's performance. The experiments demonstrate that our proposed method achieves the best results, in terms of precision, the area under the ROC curve (AUCs), and F1-score values, when compared with eight state-of-the-art network and machine learning-based disease gene prioritization methods. Conclusion This study shows that the proposed semi-supervised learning method appropriately classifies and ranks candidate disease genes using a graph convolutional network and an innovative method to create three feature vectors for genes based on the molecular function, cellular component, and biological process terms from GO data.
Collapse
Affiliation(s)
- Saeid Azadifar
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Ali Ahmadi
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
| |
Collapse
|
8
|
Chaudhuri S, Srivastava A. Network approach to understand biological systems: From single to multilayer networks. J Biosci 2022. [DOI: 10.1007/s12038-022-00285-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
9
|
Yang B, Wu J, Sun A, Gao N, Zhang X. Robust landmark graph-based clustering for high-dimensional data. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
10
|
Tayal S, Bhatia V, Mehrotra T, Bhatnagar S. ImitateDB: A database for domain and motif mimicry incorporating host and pathogen protein interactions. Amino Acids 2022; 54:923-934. [PMID: 35487995 PMCID: PMC9054641 DOI: 10.1007/s00726-022-03163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/09/2022] [Indexed: 11/26/2022]
Abstract
Molecular mimicry of host proteins by pathogens constitutes a strategy to hijack the host pathways. At present, there is no dedicated resource for mimicked domains and motifs in the host-pathogen interactome. In this work, the experimental host-pathogen (HP) and host-host (HH) protein-protein interactions (PPIs) were collated. The domains and motifs of these proteins were annotated using CD Search and ScanProsite, respectively. Host and pathogen proteins with a shared host interactor and similar domain/motif constitute a mimicry pair exhibiting global structural similarity (domain mimicry pair; DMP) or local sequence motif similarity (motif mimicry pair; MMP). Mimicry pairs are likely to be co-expressed and co-localized. 1,97,607 DMPs and 32,67,568 MMPs were identified in 49,265 experimental HP-PPIs and organized in a web-based resource, ImitateDB ( http://imitatedb.sblab-nsit.net ) that can be easily queried. The results are externally integrated using hyperlinked domain PSSM ID, motif ID, protein ID and PubMed ID. Kinase, UL36, Smc and DEXDc were frequent DMP domains whereas protein kinase C phosphorylation, casein kinase 2 phosphorylation, glycosylation and myristoylation sites were frequent MMP motifs. Novel DMP domains SANT, Tudor, PhoX and MMP motif microbody C-terminal targeting signal, cornichon signature and lipocalin signature were proposed. ImitateDB is a novel resource for identifying mimicry in interacting host and pathogen proteins.
Collapse
Affiliation(s)
- Sonali Tayal
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India
| | - Venugopal Bhatia
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India
| | - Tanya Mehrotra
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India.
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India.
| |
Collapse
|
11
|
Chatterjee B, Neelaveni K, Kenchey H, Thakur SS. An insight into major signaling pathways and protein-protein interaction networks involved in the pathogenesis of gestational diabetes mellitus. Proteomics 2022; 22:e2100200. [PMID: 35279034 DOI: 10.1002/pmic.202100200] [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: 08/26/2021] [Revised: 02/28/2022] [Accepted: 03/04/2022] [Indexed: 11/11/2022]
Abstract
Gestational diabetes mellitus (GDM) is associated with the increase of glucose in the blood rather than being absorbed by the cells. A better understanding of the signaling pathways is necessary to understand the pathophysiology of GDM. This study provides details about a series of signaling pathways and protein-protein interactions involved in the pathogenesis of GDM and their evaluations in GDM development. Protein-protein interactions were found between proteins of several signaling pathways that suggest interlink between these signaling pathways. Protein-protein interactions were generated with high confidence interaction scores based on textmining, co-occurrence, coexpression, neighborhood, gene fusion, experiments and databases. The dysregulation of signaling pathways may also contribute to the increased risk of complications associated with GDM in the mother and child. Further, studies on signaling pathways involved in the pathogenesis of GDM would help in the development of an effective intervention to prevent GDM along with the identification of key targets for effective therapies in the future. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Bhaswati Chatterjee
- National Institute of Pharmaceutical Education and Research, Hyderabad, India
| | | | - Himaja Kenchey
- Institute of Diabetes, Endocrinology and Adiposity Clinics, Hyderabad, India
| | - Suman S Thakur
- Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad, India
| |
Collapse
|
12
|
Network-Based Approach to Repurpose Approved Drugs for COVID-19 by Integrating GWAS and Text Mining Data. Processes (Basel) 2022. [DOI: 10.3390/pr10020326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The coronavirus disease 19 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a rapidly increasing prevalence and has caused significant morbidity/mortality. Despite the availability of many vaccines that can offer widespread immunization, it is also important to reach effective treatment for COVID-19 patients. However, the development of novel drug therapeutics is usually a time-consuming and costly process, and therefore, repositioning drugs that were previously approved for other purposes could have a major impact on the fight against COVID-19. Here, we first identified lung-specific gene regulatory/interaction subnetworks (COVID-19-related genes modules) enriched for COVID-19-associated genes obtained from GWAS and text mining. We then screened the targets of 220 approved drugs from DrugBank, obtained their drug-induced gene expression profiles in the LINCS database, and constructed lung-specific drug-related gene modules. By applying an integrated network-based approach to quantify the interactions of the COVID-19-related gene modules and drug-related gene modules, we prioritized 13 approved drugs (e.g., alitretinoin, clocortolone, terazosin, doconexent, and pergolide) that could potentially be repurposed for the treatment of COVID-19. These findings provide important and timely insights into alternative therapeutic options that should be further explored as COVID-19 continues to spread.
Collapse
|
13
|
Bian W, Tang M, Jiang H, Xu W, Hao W, Sui Y, Hou Y, Nie L, Zhang H, Wang C, Li N, Wang J, Qin J, Wu L, Ma X, Chen J, Wang W, Li X. Low-density-lipoprotein-receptor-related protein 1 mediates Notch pathway activation. Dev Cell 2021; 56:2902-2919.e8. [PMID: 34626540 DOI: 10.1016/j.devcel.2021.09.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 08/18/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022]
Abstract
The Notch signaling pathway controls cell growth, differentiation, and fate decisions, and its dysregulation has been linked to various human genetic disorders and cancers. To comprehensively understand the global organization of the Notch pathway and identify potential drug targets for Notch-related diseases, we established a protein interaction landscape for the human Notch pathway. By combining and analyzing genetic and phenotypic data with bioinformatics analysis, we greatly expanded this pathway and identified many key regulators, including low-density-lipoprotein-receptor-related protein 1 (LRP1). We demonstrated that LRP1 mediates the ubiquitination chain linkage switching of Delta ligands, which further affects ligand recycling, membrane localization, and stability. LRP1 inhibition led to Notch signaling inhibition and decreased tumorigenesis in leukemia models. Our study provides a glimpse into the Notch pathway interaction network and uncovers LRP1 as one critical regulator of the Notch pathway, as well as a possible therapeutic target for Notch-related cancers.
Collapse
Affiliation(s)
- Weixiang Bian
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang, China
| | - Mengfan Tang
- Department of Experimental Radiation Oncology, the University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA
| | - Hua Jiang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang, China
| | - Wenyan Xu
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Wanyu Hao
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Science, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Yue Sui
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang, China
| | - Yingnan Hou
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang, China
| | - Litong Nie
- Department of Experimental Radiation Oncology, the University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA
| | - Huimin Zhang
- Department of Experimental Radiation Oncology, the University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA
| | - Chao Wang
- Department of Experimental Radiation Oncology, the University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA
| | - Nan Li
- Department of Experimental Radiation Oncology, the University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA
| | - Jiadong Wang
- Institute of Systems Biomedicine, Department of Radiation Medicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, People's Republic of China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lianfeng Wu
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Science, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.
| | - Xianjue Ma
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
| | - Junjie Chen
- Department of Experimental Radiation Oncology, the University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA.
| | - Wenqi Wang
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA 92697, USA.
| | - Xu Li
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang, China.
| |
Collapse
|
14
|
Morgan S, Malatras A, Duguez S, Duddy W. Optimized Molecular Interaction Networks for the Study of Skeletal Muscle. J Neuromuscul Dis 2021; 8:S223-S239. [PMID: 34308911 DOI: 10.3233/jnd-210680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Molecular interaction networks (MINs) aim to capture the complex relationships between interacting molecules within a biological system. MINs can be constructed from existing knowledge of molecular functional associations, such as protein-protein binding interactions (PPI) or gene co-expression, and these different sources may be combined into a single MIN. A given MIN may be more or less optimal in its representation of the important functional relationships of molecules in a tissue. OBJECTIVE The aim of this study was to establish whether a combined MIN derived from different types of functional association could better capture muscle-relevant biology compared to its constituent single-source MINs. METHODS MINs were constructed from functional association databases for both protein-binding and gene co-expression. The networks were then compared based on the capture of muscle-relevant genes and gene ontology (GO) terms, tested in two different ways using established biological network clustering algorithms. The top performing MINs were combined to test whether an optimal MIN for skeletal muscle could be constructed. RESULTS The STRING PPI network was the best performing single-source MIN among those tested. Combining STRING with interactions from either the MyoMiner or CoXPRESSdb gene co-expression sources resulted in a combined network with improved performance relative to its constituent networks. CONCLUSION MINs constructed from multiple types of functional association can better represent the functional relationships of molecules in a given tissue. Such networks may be used to improve the analysis and interpretation of functional genomics data in the study of skeletal muscle and neuromuscular diseases. Networks and clusters described by this study, including the combinations of STRING with MyoMiner or with CoXPRESSdb, are available for download from https://www.sys-myo.com/myominer/download.php.
Collapse
Affiliation(s)
- Stephen Morgan
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| | - Apostolos Malatras
- Department of Biological Sciences, Molecular Medicine Research Center, University of Cyprus, University Avenue, Nicosia, Cyprus
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| |
Collapse
|
15
|
Das B, Mitra P. Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins. J Mol Biol 2021; 433:167149. [PMID: 34271012 DOI: 10.1016/j.jmb.2021.167149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/11/2021] [Accepted: 07/06/2021] [Indexed: 10/20/2022]
Abstract
Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of 83.45% while AUROC and AUPRC values are 0.90 and 0.88, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.
Collapse
Affiliation(s)
- Barnali Das
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur 721302, India.
| |
Collapse
|
16
|
Integration of peripheral transcriptomics, genomics, and interactomics following trauma identifies causal genes for symptoms of post-traumatic stress and major depression. Mol Psychiatry 2021; 26:3077-3092. [PMID: 33963278 DOI: 10.1038/s41380-021-01084-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 02/26/2021] [Accepted: 03/26/2021] [Indexed: 02/03/2023]
Abstract
Posttraumatic stress disorder (PTSD) is a debilitating syndrome with substantial morbidity and mortality that occurs in the aftermath of trauma. Symptoms of major depressive disorder (MDD) are also a frequent consequence of trauma exposure. Identifying novel risk markers in the immediate aftermath of trauma is a critical step for the identification of novel biological targets to understand mechanisms of pathophysiology and prevention, as well as the determination of patients most at risk who may benefit from immediate intervention. Our study utilizes a novel approach to computationally integrate blood-based transcriptomics, genomics, and interactomics to understand the development of risk vs. resilience in the months following trauma exposure. In a two-site longitudinal, observational prospective study, we assessed over 10,000 individuals and enrolled >700 subjects in the immediate aftermath of trauma (average 5.3 h post-trauma (range 0.5-12 h)) in the Grady Memorial Hospital (Atlanta) and Jackson Memorial Hospital (Miami) emergency departments. RNA expression data and 6-month follow-up data were available for 366 individuals, while genotype, transcriptome, and phenotype data were available for 297 patients. To maximize our power and understanding of genes and pathways that predict risk vs. resilience, we utilized a set-cover approach to capture fluctuations of gene expression of PTSD or depression-converting patients and non-converting trauma-exposed controls to find representative sets of disease-relevant dysregulated genes. We annotated such genes with their corresponding expression quantitative trait loci and applied a variant of a current flow algorithm to identify genes that potentially were causal for the observed dysregulation of disease genes involved in the development of depression and PTSD symptoms after trauma exposure. We obtained a final list of 11 driver causal genes related to MDD symptoms, 13 genes for PTSD symptoms, and 22 genes in PTSD and/or MDD. We observed that these individual or combined disorders shared ESR1, RUNX1, PPARA, and WWOX as driver causal genes, while other genes appeared to be causal driver in the PTSD only or MDD only cases. A number of these identified causal pathways have been previously implicated in the biology or genetics of PTSD and MDD, as well as in preclinical models of amygdala function and fear regulation. Our work provides a promising set of initial pathways that may underlie causal mechanisms in the development of PTSD or MDD in the aftermath of trauma.
Collapse
|
17
|
Vasilopoulou C, Morris AP, Giannakopoulos G, Duguez S, Duddy W. What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis? J Pers Med 2020; 10:E247. [PMID: 33256133 PMCID: PMC7712791 DOI: 10.3390/jpm10040247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.
Collapse
Affiliation(s)
- Christina Vasilopoulou
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, UK;
| | - George Giannakopoulos
- Institute of Informatics and Telecommunications, NCSR Demokritos, 153 10 Aghia Paraskevi, Greece;
- Science For You (SciFY) PNPC, TEPA Lefkippos-NCSR Demokritos, 27, Neapoleos, 153 41 Ag. Paraskevi, Greece
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| |
Collapse
|
18
|
Lee LY, Pandey AK, Maron BA, Loscalzo J. Network medicine in Cardiovascular Research. Cardiovasc Res 2020; 117:2186-2202. [PMID: 33165538 DOI: 10.1093/cvr/cvaa321] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/08/2020] [Accepted: 10/30/2020] [Indexed: 12/21/2022] Open
Abstract
The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.
Collapse
Affiliation(s)
- Laurel Y Lee
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.,Department of Cardiology, Boston VA Healthcare System, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| |
Collapse
|
19
|
Yu X, Weng T, Gu C, Yang H. Comparison of gene regulatory networks to identify pathogenic genes for lymphoma. J Bioinform Comput Biol 2020; 18:2050029. [PMID: 33131362 DOI: 10.1142/s0219720020500298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Lymphoma is the most complicated cancer that can be divided into several tens of subtypes. It may occur in any part of body that has lymphocytes, and is closely correlated with diverse environmental factors such as the ionizing radiation, chemocarcinogenesis, and virus infection. All the environmental factors affect the lymphoma through genes. Identifying pathogenic genes for lymphoma is consequently an essential task to understand its complexity in a unified framework. In this paper, we propose a new method to expose high-confident edges in gene regulatory networks (GRNs) for a total of 32 organs, called Filtered GRNs (f-GRNs), comparison of which gives us a proper reference for the Lymphoma, i.e. the B-lymphocytes cells, whose f-GRN is closest with that for the Lymphoma. By using the Gene Ontology and Biological Process analysis we display the differences of the two networks' hubs in biological functions. Matching with the Genecards shows that most of the hubs take part in the genetic information transmission and expression, except a specific gene of Retinoic Acid Receptor Alpha (RARA) that encodes the retinoic acid receptor. In the lymphoma, the genes in the RARA ego-network are involved in two cancer pathways, and the RARA is present only in these cancer pathways. For the lymphoid B cells, however, the genes in the RARA ego-network do not participate in cancer-related pathways.
Collapse
Affiliation(s)
- Xiao Yu
- Department of Systems Science, University of Shanghai for Science and Technology, Jungong Road No. 516, Shanghai 200093, P. R. China
| | - Tongfeng Weng
- Department of Systems Science, University of Shanghai for Science and Technology, Jungong Road No. 516, Shanghai 200093, P. R. China
| | - Changgui Gu
- Department of Systems Science, University of Shanghai for Science and Technology, Jungong Road No. 516, Shanghai 200093, P. R. China
| | - Huijie Yang
- Department of Systems Science, University of Shanghai for Science and Technology, Jungong Road No. 516, Shanghai 200093, P. R. China
| |
Collapse
|
20
|
Chen X, Dorris Z, Shi D, Huang RK, Khant H, Fox T, de Val N, Williams D, Zhang P, Walters KJ. Cryo-EM Reveals Unanchored M1-Ubiquitin Chain Binding at hRpn11 of the 26S Proteasome. Structure 2020; 28:1206-1217.e4. [PMID: 32783951 PMCID: PMC7642156 DOI: 10.1016/j.str.2020.07.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/06/2020] [Accepted: 07/22/2020] [Indexed: 12/17/2022]
Abstract
The 26S proteasome is specialized for regulated protein degradation and formed by a dynamic regulatory particle (RP) that caps a hollow cylindrical core particle (CP) where substrates are proteolyzed. Its diverse substrates unify as proteasome targets by ubiquitination. We used cryogenic electron microscopy (cryo-EM) to study how human 26S proteasome interacts with M1-linked hexaubiquitin (M1-Ub6) unanchored to a substrate and E3 ubiquitin ligase E6AP/UBE3A. Proteasome structures are available with model substrates extending through the RP ATPase ring and substrate-conjugated K63-linked ubiquitin chains present at inhibited deubiquitinating enzyme hRpn11 and the nearby ATPase hRpt4/hRpt5 coiled coil. In this study, we find M1-Ub6 at the hRpn11 site despite the absence of conjugated substrate, indicating that ubiquitin binding at this location does not require substrate interaction with the RP. Moreover, unanchored M1-Ub6 binds to this hRpn11 site of the proteasome with the CP gating residues in both the closed and opened conformational states.
Collapse
Affiliation(s)
- Xiang Chen
- Protein Processing Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Zachary Dorris
- Protein Processing Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Frederick High School, Frederick, MD 21702, USA
| | - Dan Shi
- Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Rick K Huang
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Htet Khant
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc, Frederick, MD 21701, USA
| | - Tara Fox
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc, Frederick, MD 21701, USA
| | - Natalia de Val
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc, Frederick, MD 21701, USA
| | - Dewight Williams
- John M. Cowley Center for High Resolution Electron Microscopy, Arizona State University, Tempe, AZ 85287, USA
| | - Ping Zhang
- Kinase Complexes Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Kylie J Walters
- Protein Processing Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| |
Collapse
|
21
|
Han Y, Cheng L, Sun W. Analysis of Protein-Protein Interaction Networks through Computational Approaches. Protein Pept Lett 2020; 27:265-278. [PMID: 31692419 DOI: 10.2174/0929866526666191105142034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 05/08/2019] [Accepted: 09/26/2019] [Indexed: 01/02/2023]
Abstract
The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.
Collapse
Affiliation(s)
- Ying Han
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weiju Sun
- Cardiovascular Department, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| |
Collapse
|
22
|
Lin Y, Afshar S, Rajadhyaksha AM, Potash JB, Han S. A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates. Front Genet 2020; 11:500064. [PMID: 33133139 PMCID: PMC7513695 DOI: 10.3389/fgene.2020.500064] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 08/13/2020] [Indexed: 11/17/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be down-expressed in ASD brains, especially in frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, NBEA, HERC1, and TCF20), but also indicated potentially novel candidates, such as, MYCBP2 and CAND1, which are involved in protein ubiquitination. We also showed that our method outperformed state-of-the-art scoring systems for ranking curated ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.
Collapse
Affiliation(s)
- Ying Lin
- Department of Industrial Engineering, University of Houston, Houston, TX, United States
| | - Shiva Afshar
- Department of Industrial Engineering, University of Houston, Houston, TX, United States
| | - Anjali M Rajadhyaksha
- Division of Pediatric Neurology, Department of Pediatrics, Weill Cornell Medicine, New York, NY, United States.,Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, New York, NY, United States.,Weill Cornell Autism Research Program, Weill Cornell Medicine, New York, NY, United States
| | - James B Potash
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Shizhong Han
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Lieber Institute for Brain Development, Baltimore, MD, United States
| |
Collapse
|
23
|
Athira K, Gopakumar G. An integrated method for identifying essential proteins from multiplex network model of protein-protein interactions. J Bioinform Comput Biol 2020; 18:2050020. [PMID: 32795133 DOI: 10.1142/s0219720020500201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cell survival requires the presence of essential proteins. Detection of essential proteins is relevant not only because of the critical biological functions they perform but also the role played by them as a drug target against pathogens. Several computational techniques are in place to identify essential proteins based on protein-protein interaction (PPI) network. Essential protein detection using only physical interaction data of proteins is challenging due to its inherent uncertainty. Hence, in this work, we propose a multiplex network-based framework that incorporates multiple protein interaction data from their physical, coexpression and phylogenetic profiles. An extended version termed as multiplex eigenvector centrality (MEC) is used to identify essential proteins from this network. The methodology integrates the score obtained from the multiplex analysis with subcellular localization and Gene Ontology information and is implemented using Saccharomyces cerevisiae datasets. The proposed method outperformed many recent essential protein prediction techniques in the literature.
Collapse
Affiliation(s)
- K Athira
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikkode, Kerala 673601, India
| | - G Gopakumar
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikkode, Kerala 673601, India
| |
Collapse
|
24
|
Hristov BH, Chazelle B, Singh M. uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes. Cell Syst 2020; 10:470-479.e3. [PMID: 32684276 PMCID: PMC7821437 DOI: 10.1016/j.cels.2020.05.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/24/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022]
Abstract
Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.
Collapse
Affiliation(s)
- Borislav H Hristov
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Bernard Chazelle
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
| |
Collapse
|
25
|
Bean DM, Al-Chalabi A, Dobson RJB, Iacoangeli A. A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis. Genes (Basel) 2020; 11:E668. [PMID: 32575372 PMCID: PMC7349022 DOI: 10.3390/genes11060668] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/13/2020] [Accepted: 06/16/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar p = 0.038 and manual review p = 0.060) when used for gene prioritisation in a genome-wide association study.
Collapse
Affiliation(s)
- Daniel M. Bean
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
| | - Ammar Al-Chalabi
- King′s College Hospital, Bessemer Road, Denmark Hill, Brixton, London SE5 9RS, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
| | - Richard J. B. Dobson
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, UK
| | - Alfredo Iacoangeli
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
| |
Collapse
|
26
|
Liu L, Huang X, Mamitsuka H, Zhu S. HPOLabeler: improving prediction of human protein–phenotype associations by learning to rank. Bioinformatics 2020; 36:4180-4188. [DOI: 10.1093/bioinformatics/btaa284] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 04/05/2020] [Accepted: 04/30/2020] [Indexed: 12/23/2022] Open
Abstract
Abstract
Motivation
Annotating human proteins by abnormal phenotypes has become an important topic. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities encountered in human diseases. As of November 2019, only <4000 proteins have been annotated with HPO. Thus, a computational approach for accurately predicting protein–HPO associations would be important, whereas no methods have outperformed a simple Naive approach in the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2).
Results
We present HPOLabeler, which is able to use a wide variety of evidence, such as protein–protein interaction (PPI) networks, Gene Ontology, InterPro, trigram frequency and HPO term frequency, in the framework of learning to rank (LTR). LTR has been proved to be powerful for solving large-scale, multi-label ranking problems in bioinformatics. Given an input protein, LTR outputs the ranked list of HPO terms from a series of input scores given to the candidate HPO terms by component learning models (logistic regression, nearest neighbor and a Naive method), which are trained from given multiple evidence. We empirically evaluate HPOLabeler extensively through mainly two experiments of cross validation and temporal validation, for which HPOLabeler significantly outperformed all component models and competing methods including the current state-of-the-art method. We further found that (i) PPI is most informative for prediction among diverse data sources and (ii) low prediction performance of temporal validation might be caused by incomplete annotation of new proteins.
Availability and implementation
http://issubmission.sjtu.edu.cn/hpolabeler/.
Contact
zhusf@fudan.edu.cn
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Lizhi Liu
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing
- Shanghai Institute of Artificial Intelligence Algorithms and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaodi Huang
- School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Shanfeng Zhu
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing
- Shanghai Institute of Artificial Intelligence Algorithms and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| |
Collapse
|
27
|
Gysi DM, Nowick K. Construction, comparison and evolution of networks in life sciences and other disciplines. J R Soc Interface 2020; 17:20190610. [PMID: 32370689 PMCID: PMC7276545 DOI: 10.1098/rsif.2019.0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
Collapse
Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, 04109 Leipzig, Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, 04109 Leipzig, Germany
- Center for Complex Networks Research, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Faculty of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Straβe 1-3, 14195 Berlin, Germany
| |
Collapse
|
28
|
Silverman EK, Schmidt HHHW, Anastasiadou E, Altucci L, Angelini M, Badimon L, Balligand JL, Benincasa G, Capasso G, Conte F, Di Costanzo A, Farina L, Fiscon G, Gatto L, Gentili M, Loscalzo J, Marchese C, Napoli C, Paci P, Petti M, Quackenbush J, Tieri P, Viggiano D, Vilahur G, Glass K, Baumbach J. Molecular networks in Network Medicine: Development and applications. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1489. [PMID: 32307915 DOI: 10.1002/wsbm.1489] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/29/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022]
Abstract
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
Collapse
Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | - Eleni Anastasiadou
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Angelini
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lina Badimon
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research (IREC), UCLouvain, Brussels, Belgium
| | - Giuditta Benincasa
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy.,BIOGEM, Ariano Irpino, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Antonella Di Costanzo
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Laurent Gatto
- de Duve Institute, Brussels, Belgium.,Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
| | - Michele Gentili
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Claudio Napoli
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paolo Tieri
- CNR National Research Council of Italy, IAC Institute for Applied Computing, Rome, Italy
| | - Davide Viggiano
- BIOGEM, Ariano Irpino, Italy.,Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Gemma Vilahur
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jan Baumbach
- Department of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, Freising, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
29
|
Garapati HS, Male G, Mishra K. Predicting subcellular localization of proteins using protein-protein interaction data. Genomics 2020; 112:2361-2368. [PMID: 31945465 DOI: 10.1016/j.ygeno.2020.01.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 01/01/2020] [Accepted: 01/11/2020] [Indexed: 10/25/2022]
Abstract
The knowledge of subcellular localization of proteins can provide useful clues about their functions. The conventional methods to determine the subcellular localization are unable to keep pace with the rate at which the new data is being generated. Thus, though sequence information is available, the localization and function of a number of proteins remains unknown. In this study, we have developed a script that makes use of the physical interactors of a protein and their localization data to predict the subcellular localization. We used the script to predict the localization of yeast proteins for which there is no localization data. Further, we experimentally verified the predicted localization for six arbitrarily chosen proteins and found our predictions to be correct for five of the proteins.
Collapse
Affiliation(s)
- Hita Sony Garapati
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India
| | - Gurranna Male
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India
| | - Krishnaveni Mishra
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India.
| |
Collapse
|
30
|
Byska J, Jurcik A, Furmanova K, Kozlikova B, Palecek JJ. Visual Analysis of Protein-Protein Interaction Docking Models Using COZOID Tool. Methods Mol Biol 2020; 2074:81-94. [PMID: 31583632 DOI: 10.1007/978-1-4939-9873-9_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Networks of protein-protein interactions (PPI) constitute either stable or transient complexes in every cell. Most of the cellular complexes keep their function, and therefore stay similar, during evolution. The evolutionary constraints preserve most cellular functions via preservation of protein structures and interactions. The evolutionary conservation information is utilized in template-based approaches, like protein structure modeling or docking. Here we use the combination of the template-free docking method with conservation-based selection of the best docking model using our newly developed COZOID tool.We describe a step-by-step protocol for visual selection of docking models, based on their similarity to the original protein complex structure. Using the COZOID tool, we first analyze contact zones of the original complex structure and select contact amino acids for docking restraints. Then we model and dock the homologous proteins. Finally, we utilize different analytical modes of our COZOID tool to select the docking models most similar to the original complex structure.
Collapse
Affiliation(s)
- Jan Byska
- Department of Informatics, University of Bergen, Bergen, Norway
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Adam Jurcik
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | | | | | - Jan J Palecek
- Faculty of Science, National Centre for Biomolecular Research, Masaryk University, Brno, Czech Republic.
- Mendel Centre for Plant Genomics and Proteomics, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
| |
Collapse
|
31
|
Aguilar D, Lemonnier N, Koppelman GH, Melén E, Oliva B, Pinart M, Guerra S, Bousquet J, Anto JM. Understanding allergic multimorbidity within the non-eosinophilic interactome. PLoS One 2019; 14:e0224448. [PMID: 31693680 PMCID: PMC6834334 DOI: 10.1371/journal.pone.0224448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/14/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
Collapse
MESH Headings
- Asthma/epidemiology
- Asthma/genetics
- Asthma/immunology
- Blood Cells/immunology
- Blood Cells/metabolism
- Cytokines/immunology
- Cytokines/metabolism
- Datasets as Topic
- Dermatitis, Allergic Contact/epidemiology
- Dermatitis, Allergic Contact/genetics
- Dermatitis, Allergic Contact/immunology
- Dermatitis, Atopic/epidemiology
- Dermatitis, Atopic/genetics
- Dermatitis, Atopic/immunology
- Gene Expression Profiling
- Humans
- Immunity, Cellular/genetics
- Multimorbidity
- Protein Interaction Maps/genetics
- Protein Interaction Maps/immunology
- Rhinitis, Allergic/epidemiology
- Rhinitis, Allergic/genetics
- Rhinitis, Allergic/immunology
Collapse
Affiliation(s)
- Daniel Aguilar
- Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- 6AM Data Mining, Barcelona, Spain
| | - Nathanael Lemonnier
- Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France
| | - Gerard H. Koppelman
- University of Groningen, University Medical Center Groningen, Beatrix Children’s Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mariona Pinart
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| | - Stefano Guerra
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America
| | - Jean Bousquet
- Hopital Arnaud de Villeneuve University Hospital, Montpellier, France
- Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
| | - Josep M. Anto
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| |
Collapse
|
32
|
Lugo-Martinez J, Bar-Joseph Z, Dengjel J, Murphy RF. Integration of Heterogeneous Experimental Data Improves Global Map of Human Protein Complexes. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2019; 2019:144-153. [PMID: 32457940 DOI: 10.1145/3307339.3342150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein complexes play a significant role in the core functionality of cells. These complexes are typically identified by detecting densely connected subgraphs in protein-protein interaction (PPI) networks. Recently, multiple large-scale mass spectrometry-based experiments have significantly increased the availability of PPI data in order to further expand the set of known complexes. However, high-throughput experimental data generally are incomplete, show limited agreement between experiments, and show frequent false positive interactions. There is a need for computational approaches that can address these limitations in order to improve the coverage and accuracy of human protein complexes. Here, we present a new method that integrates data from multiple heterogeneous experiments and sources in order to increase the reliability and coverage of predicted protein complexes. We first fused the heterogeneous data into a feature matrix and trained classifiers to score pairwise protein interactions. We next used graph based methods to combine pairwise interactions into predicted protein complexes. Our approach improves the accuracy and coverage of protein pairwise interactions, accurately identifies known complexes, and suggests both novel additions to known complexes and entirely new complexes. Our results suggest that integration of heterogeneous experimental data helps improve the reliability and coverage of diverse high-throughput mass-spectrometry experiments, leading to an improved global map of human protein complexes.
Collapse
Affiliation(s)
- Jose Lugo-Martinez
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA
| | - Ziv Bar-Joseph
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA
| | - Jörn Dengjel
- Department of Biology, Université de Fribourg, 1700 Fribourg, Switzerland
| | - Robert F Murphy
- Computational Biology Department, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA
| |
Collapse
|
33
|
GPS: Identification of disease genes by rank aggregation of multi-genomic scoring schemes. Genomics 2019; 111:612-618. [DOI: 10.1016/j.ygeno.2018.03.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 03/16/2018] [Accepted: 03/21/2018] [Indexed: 12/19/2022]
|
34
|
Ackerman EE, Alcorn JF, Hase T, Shoemaker JE. A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery. BMC Bioinformatics 2019; 20:297. [PMID: 31159726 PMCID: PMC6545738 DOI: 10.1186/s12859-019-2917-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 05/28/2019] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell's progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates. RESULTS Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. CONCLUSIONS Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.
Collapse
Affiliation(s)
- Emily E Ackerman
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - John F Alcorn
- Division of Pulmonary Medicine, Allergy, and Immunology, Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - Takeshi Hase
- The Systems Biology Institute, Saisei Ikedayama Bldg. 5-10-25 Higashi Gotanda, Shinagawa, Tokyo, 141-0022, Japan
- Medical Data Sciences Office, Tokyo Medical and Dental University, M&D Tower 20F, 1-5-45 Yushima, Bunkyo, Tokyo, 113-8510, Japan
| | - Jason E Shoemaker
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- The McGowan Institute for Regenerative Medicine (MIRM), University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| |
Collapse
|
35
|
Substrate complex competition is a regulatory motif that allows NFκB RelA to license but not amplify NFκB RelB. Proc Natl Acad Sci U S A 2019; 116:10592-10597. [PMID: 31048505 DOI: 10.1073/pnas.1816000116] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NFκB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (IκBδ) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose-response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis-Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context.
Collapse
|
36
|
Ahsan A, Monir M, Meng X, Rahaman M, Chen H, Chen M. Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect. DNA Res 2019; 26:119-130. [PMID: 30590457 PMCID: PMC6476725 DOI: 10.1093/dnares/dsy043] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 11/21/2018] [Indexed: 01/28/2023] Open
Abstract
Flowering time is an important agronomic trait, attributed by multiple genes, gene-gene interactions and environmental factors. Population stratification and polygenic effects might confound genetic effects of the causal loci underlying this complex trait. We proposed a two-step approach for detecting epistasis interactions underlying rice flowering time by accounting population structure and polygenic effects. Simulation studies showed that the approach used in this study performs better than classical and PC-linear approaches in terms of powers and false discovery rates in the case of population stratification and polygenic effects. Whole genome epistasis analyses identified 589 putative genetic interactions for flowering time. Eighteen of these interactions are located within 10 kilobases of regions of known protein-protein interactions. Thirty-seven SNPs near to twenty-five genes involve in rice or/and Arabidopsis (orthologue) flowering pathway. Bioinformatics analysis showed that 66.55% pairwise genes of the identified interactions (392 out of the 589 interactions) have similarity in various genomic features. Moreover, significant numbers of detected epistatic genes have high expression in different floral tissues. Our findings highlight the importance of epistasis analysis by controlling population stratification and polygenic effect and provided novel insights into the genetic architecture of rice flowering which could assist breeding programmes.
Collapse
Affiliation(s)
- Asif Ahsan
- The State Key Laboratory of Plant Physiology and Biochemistry, Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Mamun Monir
- Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Xianwen Meng
- The State Key Laboratory of Plant Physiology and Biochemistry, Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Matiur Rahaman
- The State Key Laboratory of Plant Physiology and Biochemistry, Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Hongjun Chen
- The State Key Laboratory of Plant Physiology and Biochemistry, Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Ming Chen
- The State Key Laboratory of Plant Physiology and Biochemistry, Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
- Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| |
Collapse
|
37
|
Kovács IA, Luck K, Spirohn K, Wang Y, Pollis C, Schlabach S, Bian W, Kim DK, Kishore N, Hao T, Calderwood MA, Vidal M, Barabási AL. Network-based prediction of protein interactions. Nat Commun 2019; 10:1240. [PMID: 30886144 PMCID: PMC6423278 DOI: 10.1038/s41467-019-09177-y] [Citation(s) in RCA: 161] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 02/22/2019] [Indexed: 12/15/2022] Open
Abstract
Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other's partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome.
Collapse
Affiliation(s)
- István A Kovács
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
- Wigner Research Centre for Physics, Institute for Solid State Physics and Optics, H-1525, Budapest, P.O.Box 49, Hungary.
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Carl Pollis
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Sadie Schlabach
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Wenting Bian
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Dae-Kyum Kim
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Donnelly Centre, Toronto, Ontario, Canada, Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Nishka Kishore
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Donnelly Centre, Toronto, Ontario, Canada, Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
- Division of Network Medicine and Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary.
| |
Collapse
|
38
|
Fabre KM, Delsing L, Hicks R, Colclough N, Crowther DC, Ewart L. Utilizing microphysiological systems and induced pluripotent stem cells for disease modeling: a case study for blood brain barrier research in a pharmaceutical setting. Adv Drug Deliv Rev 2019; 140:129-135. [PMID: 30253201 DOI: 10.1016/j.addr.2018.09.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 09/06/2018] [Accepted: 09/19/2018] [Indexed: 12/21/2022]
Abstract
Microphysiological systems (MPS) may be able to provide the pharmaceutical industry models that can reflect human physiological responses to improve drug discovery and translational outcomes. With lack of efficacy being the primary cause for drug attrition, developing MPS disease models would help researchers identify novel targets, study mechanisms in more physiologically-relevant depth, screen for novel biomarkers and test/optimize various therapeutics (small molecules, nanoparticles and biologics). Furthermore, with advances in inducible pluripotent stem cell technology (iPSC), pharmaceutical companies can access cells from patients to help recreate specific disease phenotypes in MPS platforms. Combining iPSC and MPS technologies will contribute to our understanding of the complexities of neurodegenerative diseases and of the blood brain barrier (BBB) leading to development of enhanced therapeutics.
Collapse
|
39
|
Rehman AU, Rahman MU, Arshad T, Chen HF. Allosteric Modulation of Intrinsically Disordered Proteins. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1163:335-357. [PMID: 31707710 DOI: 10.1007/978-981-13-8719-7_14] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The allosteric property of globular proteins is applauded as their intrinsic ability to regulate distant sites, and this property further plays a critical role in a wide variety of cellular regulatory mechanisms. Recent advancements and studies have revealed the manifestation of allostery in intrinsically disordered proteins or regions as allosteric sites present within or mediated by IDP/IDRs facilitates the signaling interactions for various biological mechanisms which would otherwise be impossible for globular proteins to regulate. This thematic review has highlighted the biological outcomes that can be achieved by the mechanism of allosteric regulation of intrinsically disordered proteins or regions. The similar mechanism has been implemented on Adenovirus 5 early region 1A and tumor apoptosis protein p53 in correspondence with other partners in binary and ternary complexes, which are the subject of the current review. Both these proteins regulate once they bind to their partners, consequently, forming either a binary or a ternary complex. Allosteric regulation by IDPs is currently a subject undergoing intense study, and the ongoing research work will ensure a better understanding of precision and efficiency of cellular regulation by them. Allosteric regulation mechanism can also be researched by intrinsically disordered protein-specific force field.
Collapse
Affiliation(s)
- Ashfaq Ur Rehman
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Mueed Ur Rahman
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Taaha Arshad
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Center for Bioinformation Technology, Shanghai, China.
| |
Collapse
|
40
|
Zhang G, Zhang W. Protein-protein interaction network analysis of insecticide resistance molecular mechanism in Drosophila melanogaster. ARCHIVES OF INSECT BIOCHEMISTRY AND PHYSIOLOGY 2019; 100:e21523. [PMID: 30478906 DOI: 10.1002/arch.21523] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Revised: 10/15/2018] [Accepted: 10/27/2018] [Indexed: 06/09/2023]
Abstract
The problem of resistance has not been solved fundamentally at present, because the development speed of new insecticides can not keep pace with the development speed of resistance, and the lack of understanding of molecular mechanism of resistance. Here we collected seed genes and their interacting proteins involved in insecticide resistance molecular mechanism in Drosophila melanogaster by literature mining and the String database. We identified a total of 528 proteins and 13514 protein-protein interactions. The protein interaction network was constructed by String and Pajek, and we analyzed the topological properties, such as degree centrality and eigenvector centrality. Proteasome complexes and drug metabolism-cytochrome P450 were an enrichment by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. This is the first time to explore the insecticide resistance molecular mechanism of D. melanogaster by the methods and tools of network biology, it can provide the bioinformatic foundation for further understanding the mechanisms of insecticide resistance.
Collapse
Affiliation(s)
- GuiLu Zhang
- School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - WenJun Zhang
- School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| |
Collapse
|
41
|
Capriotti E, Ozturk K, Carter H. Integrating molecular networks with genetic variant interpretation for precision medicine. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1443. [PMID: 30548534 PMCID: PMC6450710 DOI: 10.1002/wsbm.1443] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/23/2018] [Accepted: 10/30/2018] [Indexed: 02/01/2023]
Abstract
More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under:
Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods
Collapse
Affiliation(s)
- Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Kivilcim Ozturk
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, California
| | - Hannah Carter
- Department of Medicine and Institute for Genomic Medicine, University of California, San Diego, La Jolla, California
| |
Collapse
|
42
|
Miraee-Nedjad S, Sims PFG, Schwartz JM, Doig AJ. Effect of IAPP on the proteome of cultured Rin-5F cells. BMC BIOCHEMISTRY 2018; 19:9. [PMID: 30419808 PMCID: PMC6233276 DOI: 10.1186/s12858-018-0099-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 10/22/2018] [Indexed: 11/12/2022]
Abstract
Background Islet amyloid polypeptide (IAPP) or amylin deposits can be found in the islets of type 2 diabetes patients. The peptide is suggested to be involved in the etiology of the disease through formation of amyloid deposits and destruction of β islet cells, though the underlying molecular events leading from IAPP deposition to β cell death are still largely unknown. Results We used OFFGEL™ proteomics to study how IAPP exposure affects the proteome of rat pancreatic insulinoma Rin-5F cells. The OFFGEL™ methodology is highly effective at generating quantitative data on hundreds of proteins affected by IAPP, with its accuracy confirmed by In Cell Western and Quantitative Real Time PCR results. Combining data on individual proteins identifies pathways and protein complexes affected by IAPP. IAPP disrupts protein synthesis and degradation, and induces oxidative stress. It causes decreases in protein transport and localization. IAPP disrupts the regulation of ubiquitin-dependent protein degradation and increases catabolic processes. IAPP causes decreases in protein transport and localization, and affects the cytoskeleton, DNA repair and oxidative stress. Conclusions Results are consistent with a model where IAPP aggregates overwhelm the ability of a cell to degrade proteins via the ubiquitin system. Ultimately this leads to apoptosis. IAPP aggregates may be also toxic to the cell by causing oxidative stress, leading to DNA damage or by decreasing protein transport. The reversal of any of these effects, perhaps by targeting proteins which alter in response to IAPP, may be beneficial for type II diabetes. Electronic supplementary material The online version of this article (10.1186/s12858-018-0099-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Samaneh Miraee-Nedjad
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Paul F G Sims
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Jean-Marc Schwartz
- Division of Evolution & Genomic Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
| | - Andrew J Doig
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK.
| |
Collapse
|
43
|
Boudellioua I, Kulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R. OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants. Sci Rep 2018; 8:14681. [PMID: 30279426 PMCID: PMC6168481 DOI: 10.1038/s41598-018-32876-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 09/18/2018] [Indexed: 12/12/2022] Open
Abstract
An increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.
Collapse
Affiliation(s)
- Imane Boudellioua
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Maxat Kulmanov
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Paul N Schofield
- Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, Birmingham, United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, B15 2TT, Birmingham, United Kingdom
- NIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, B15 2TT, Birmingham, UK
- NIHR Biomedical Research Centre, B15 2TT, Birmingham, UK
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
| |
Collapse
|
44
|
Anchoori RK, Jiang R, Peng S, Soong RS, Algethami A, Rudek MA, Anders N, Hung CF, Chen X, Lu X, Kayode O, Dyba M, Walters KJ, Roden RBS. Covalent Rpn13-Binding Inhibitors for the Treatment of Ovarian Cancer. ACS OMEGA 2018; 3:11917-11929. [PMID: 30288466 PMCID: PMC6166221 DOI: 10.1021/acsomega.8b01479] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 08/29/2018] [Indexed: 06/08/2023]
Abstract
Substitution of the m,p-chloro groups of bis-benzylidinepiperidone RA190 for p-nitro, generating RA183, enhanced covalent drug binding to Cys88 of RPN13. Treatment of cancer cell lines with RA183 inhibited ubiquitin-mediated protein degradation, resulting in rapid accumulation of high-molecular-weight polyubiquitinated proteins, blockade of NFκB signaling, endoplasmic reticulum stress, an unfolded protein response, production of reactive oxygen species, and apoptotic cell death. High-grade ovarian cancer, triple-negative breast cancer, and multiple myeloma cell lines were particularly vulnerable to RA183. RA183 stabilized a tetraubiquitin-linked firefly luciferase reporter protein in cancer cell lines and mice, demonstrating in vitro and in vivo proteasomal inhibition, respectively. However, RA183 was rapidly cleared from plasma, likely reflecting its rapid degradation to the active compound RA9, as seen in human liver microsomes. Intraperitoneal administration of RA183 inhibited proteasome function and orthotopic tumor growth in mice bearing human ovarian cancer model ES2-luc ascites or syngeneic ID8-luc tumor.
Collapse
Affiliation(s)
- Ravi K. Anchoori
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Rosie Jiang
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Shiwen Peng
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Ruey-shyang Soong
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
- Department of General Surgery, Chang Gung
Memorial Hospital at Keelung, Keelung
City, Taiwan 204, ROC
- College of Medicine, Chang Gung University, Taoyuan, Taiwan 33302, ROC
| | - Aliyah Algethami
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Michelle A. Rudek
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Nicole Anders
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Chien-Fu Hung
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Xiang Chen
- Protein Processing Section, Biophysics Resource, and Basic Science
Program, Leidos Biomedical Research, Inc., Biophysics Laboratory, Center for Cancer Research, National Cancer
Institute, Frederick, Maryland 21702, United States
| | - Xiuxiu Lu
- Protein Processing Section, Biophysics Resource, and Basic Science
Program, Leidos Biomedical Research, Inc., Biophysics Laboratory, Center for Cancer Research, National Cancer
Institute, Frederick, Maryland 21702, United States
| | - Olumide Kayode
- Protein Processing Section, Biophysics Resource, and Basic Science
Program, Leidos Biomedical Research, Inc., Biophysics Laboratory, Center for Cancer Research, National Cancer
Institute, Frederick, Maryland 21702, United States
| | - Marzena Dyba
- Protein Processing Section, Biophysics Resource, and Basic Science
Program, Leidos Biomedical Research, Inc., Biophysics Laboratory, Center for Cancer Research, National Cancer
Institute, Frederick, Maryland 21702, United States
| | - Kylie J. Walters
- Protein Processing Section, Biophysics Resource, and Basic Science
Program, Leidos Biomedical Research, Inc., Biophysics Laboratory, Center for Cancer Research, National Cancer
Institute, Frederick, Maryland 21702, United States
| | - Richard B. S. Roden
- Department
of Oncology, Department of Pathology, and Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, Maryland 21231, United States
| |
Collapse
|
45
|
Mirzaei Mehrabad E, Hassanzadeh R, Eslahchi C. PMLPR: A novel method for predicting subcellular localization based on recommender systems. Sci Rep 2018; 8:12006. [PMID: 30104743 PMCID: PMC6089892 DOI: 10.1038/s41598-018-30394-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 07/30/2018] [Indexed: 12/16/2022] Open
Abstract
The importance of protein subcellular localization problem is due to the importance of protein's functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du et al., DBMLoc and Höglund. The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.
Collapse
Affiliation(s)
- Elnaz Mirzaei Mehrabad
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Reza Hassanzadeh
- Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran
- Department of Bioinformatics, Faculty of Computer Engineering and Information Technology, Sabalan University of Advanced Technologies (SUAT), Namin, Iran
| | - Changiz Eslahchi
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| |
Collapse
|
46
|
Gonçalves VF, Cappi C, Hagen CM, Sequeira A, Vawter MP, Derkach A, Zai CC, Hedley PL, Bybjerg-Grauholm J, Pouget JG, Cuperfain AB, Sullivan PF, Christiansen M, Kennedy JL, Sun L. A Comprehensive Analysis of Nuclear-Encoded Mitochondrial Genes in Schizophrenia. Biol Psychiatry 2018; 83:780-789. [PMID: 29628042 PMCID: PMC7168759 DOI: 10.1016/j.biopsych.2018.02.1175] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 02/26/2018] [Accepted: 02/27/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND The genetic risk factors of schizophrenia (SCZ), a severe psychiatric disorder, are not yet fully understood. Multiple lines of evidence suggest that mitochondrial dysfunction may play a role in SCZ, but comprehensive association studies are lacking. We hypothesized that variants in nuclear-encoded mitochondrial genes influence susceptibility to SCZ. METHODS We conducted gene-based and gene-set analyses using summary association results from the Psychiatric Genomics Consortium Schizophrenia Phase 2 (PGC-SCZ2) genome-wide association study comprising 35,476 cases and 46,839 control subjects. We applied the MAGMA method to three sets of nuclear-encoded mitochondrial genes: oxidative phosphorylation genes, other nuclear-encoded mitochondrial genes, and genes involved in nucleus-mitochondria crosstalk. Furthermore, we conducted a replication study using the iPSYCH SCZ sample of 2290 cases and 21,621 control subjects. RESULTS In the PGC-SCZ2 sample, 1186 mitochondrial genes were analyzed, among which 159 had p values < .05 and 19 remained significant after multiple testing correction. A meta-analysis of 818 genes combining the PGC-SCZ2 and iPSYCH samples resulted in 104 nominally significant and nine significant genes, suggesting a polygenic model for the nuclear-encoded mitochondrial genes. Gene-set analysis, however, did not show significant results. In an in silico protein-protein interaction network analysis, 14 mitochondrial genes interacted directly with 158 SCZ risk genes identified in PGC-SCZ2 (permutation p = .02), and aldosterone signaling in epithelial cells and mitochondrial dysfunction pathways appeared to be overrepresented in this network of mitochondrial and SCZ risk genes. CONCLUSIONS This study provides evidence that specific aspects of mitochondrial function may play a role in SCZ, but we did not observe its broad involvement even using a large sample.
Collapse
Affiliation(s)
- Vanessa F Gonçalves
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
| | - Carolina Cappi
- Department of Psychiatry, University of São Paulo, School of Medicine, Brazil
| | - Christian M Hagen
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Adolfo Sequeira
- Department of Psychiatry and Human Behavior, University of California, Irvine, USA
| | - Marquis P Vawter
- Department of Psychiatry and Human Behavior, University of California, Irvine, USA
| | - Andriy Derkach
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada
| | - Clement C Zai
- Department of Psychiatry, University of Toronto, Toronto, Canada,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Canada
| | - Paula L Hedley
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | | | - Jennie G Pouget
- Department of Psychiatry, University of Toronto, Toronto, Canada,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Canada
| | - Ari B. Cuperfain
- Department of Psychiatry, University of Toronto, Toronto, Canada,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Canada
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA;,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Michael Christiansen
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark,Department of Biomedical Science, University of Copenhagen, Copenhagen, Denmark
| | - James L Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Canada,Neuroscience Section, Centre for Addiction and Mental Health, Toronto, Canada,Corresponding author: Vanessa F Gonçalves ()
| | - Lei Sun
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada,Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada,Corresponding author: Vanessa F Gonçalves ()
| |
Collapse
|
47
|
Lv L, Huang B, Zhao Q, Zhao Z, Dong H, Zhu S, Chen T, Yan M, Han H. Identification of an interaction between calcium-dependent protein kinase 4 (EtCDPK4) and serine protease inhibitor (EtSerpin) in Eimeria tenella. Parasit Vectors 2018; 11:259. [PMID: 29688868 PMCID: PMC5913893 DOI: 10.1186/s13071-018-2848-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 04/13/2018] [Indexed: 11/10/2022] Open
Abstract
Background Eimeria tenella is an obligate intracellular apicomplexan protozoan parasite that has a complex life-cycle. Calcium ions, through various calcium-dependent protein kinases (CDPKs), regulate key events in parasite growth and development, including protein secretion, movement, differentiation, and invasion of and escape from host cells. In this study, we identified proteins that interact with EtCDPK4 to lay a foundation for clarifying the role of CDPKs in calcium channels. Methods Eimeria tenella merozoites were collected to construct a yeast two-hybrid (Y2H) cDNA library. The Y2H system was used to identify proteins that interact with EtCDPK4. One of interacting proteins was confirmed using bimolecular fluorescence complementation and co-immunoprecipitation in vivo. Co-localization of proteins was performed using immunofluorescence assays. Results Eight proteins that interact with EtCDPK4 were identified using the Y2H system. One of the proteins, E. tenella serine protease inhibitor 1 (EtSerpin), was further confirmed. Conclusion In this study, we screened for proteins that interact with EtCDPK4. An interaction between EtSerpin and EtCDPK4 was identified that may contribute to the invasion and development of E. tenella in host cells.
Collapse
Affiliation(s)
- Ling Lv
- College of Life and Environment Sciences, Shanghai Normal University, Shanghai, 200234, China.,Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Bing Huang
- College of Life and Environment Sciences, Shanghai Normal University, Shanghai, 200234, China.,Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Qiping Zhao
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Zongping Zhao
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Hui Dong
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Shunhai Zhu
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Ting Chen
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Ming Yan
- College of Life and Environment Sciences, Shanghai Normal University, Shanghai, 200234, China.,Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China
| | - Hongyu Han
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal Parasitology of Ministry of Agriculture, Minhang, Shanghai, 200241, People's Republic of China.
| |
Collapse
|
48
|
Zhang Y, Liu J, Liu X, Fan X, Hong Y, Wang Y, Huang Y, Xie M. Prioritizing disease genes with an improved dual label propagation framework. BMC Bioinformatics 2018; 19:47. [PMID: 29422030 PMCID: PMC5806269 DOI: 10.1186/s12859-018-2040-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 01/24/2018] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Prioritizing disease genes is trying to identify potential disease causing genes for a given phenotype, which can be applied to reveal the inherited basis of human diseases and facilitate drug development. Our motivation is inspired by label propagation algorithm and the false positive protein-protein interactions that exist in the dataset. To the best of our knowledge, the false positive protein-protein interactions have not been considered before in disease gene prioritization. Label propagation has been successfully applied to prioritize disease causing genes in previous network-based methods. These network-based methods use basic label propagation, i.e. random walk, on networks to prioritize disease genes in different ways. However, all these methods can not deal with the situation in which plenty false positive protein-protein interactions exist in the dataset, because the PPI network is used as a fixed input in previous methods. This important characteristic of data source may cause a large deviation in results. RESULTS A novel network-based framework IDLP is proposed to prioritize candidate disease genes. IDLP effectively propagates labels throughout the PPI network and the phenotype similarity network. It avoids the method falling when few disease genes are known. Meanwhile, IDLP models the bias caused by false positive protein interactions and other potential factors by treating the PPI network matrix and the phenotype similarity matrix as the matrices to be learnt. By amending the noises in training matrices, it improves the performance results significantly. We conduct extensive experiments over OMIM datasets, and IDLP has demonstrated its effectiveness compared with eight state-of-the-art approaches. The robustness of IDLP is also validated by doing experiments with disturbed PPI network. Furthermore, We search the literatures to verify the predicted new genes got by IDLP are associated with the given diseases, the high prediction accuracy shows IDLP can be a powerful tool to help biologists discover new disease genes. CONCLUSIONS IDLP model is an effective method for disease gene prioritization, particularly for querying phenotypes without known associated genes, which would be greatly helpful for identifying disease genes for less studied phenotypes. AVAILABILITY https://github.com/nkiip/IDLP.
Collapse
Affiliation(s)
- Yaogong Zhang
- College of Software, Nankai University, TianJin, 300350, China
| | - Jiahui Liu
- College of Software, Nankai University, TianJin, 300350, China
| | - Xiaohu Liu
- College of Software, Nankai University, TianJin, 300350, China
| | - Xin Fan
- College of Software, Nankai University, TianJin, 300350, China
| | - Yuxiang Hong
- College of Software, Nankai University, TianJin, 300350, China
| | - Yuan Wang
- School of Computer Science and Information Engineering, Tianjin University of Science and Technology, TianJin, 300222, China
| | - YaLou Huang
- College of Software, Nankai University, TianJin, 300350, China
| | - MaoQiang Xie
- College of Software, Nankai University, TianJin, 300350, China.
| |
Collapse
|
49
|
Recent progress in mass spectrometry proteomics for biomedical research. SCIENCE CHINA-LIFE SCIENCES 2017; 60:1093-1113. [DOI: 10.1007/s11427-017-9175-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 09/15/2017] [Indexed: 12/30/2022]
|
50
|
Wang X, Jin Y. Predicted networks of protein-protein interactions in Stegodyphus mimosarum by cross-species comparisons. BMC Genomics 2017; 18:716. [PMID: 28893204 PMCID: PMC5594591 DOI: 10.1186/s12864-017-4085-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 08/23/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stegodyphus mimosarum is a candidate model organism belonging to the class Arachnida in the phylum Arthropoda. Studies on the biology of S. mimosarum over the past several decades have consisted of behavioral research and comparison of gene sequences based on the assembled genome sequence. Given the lack of systematic protein analyses and the rich source of information in the genome, we predicted the relationships of proteins in S. mimosarum by bioinformatics comparison with genome-wide proteins from select model organisms using gene mapping. RESULTS The protein-protein interactions (PPIs) of 11 organisms were integrated from four databases (BioGrid, InAct, MINT, and DIP). Here, we present comprehensive prediction and analysis of 3810 proteins in S. mimosarum with regard to interactions between proteins using PPI data of organisms. Interestingly, a portion of the protein interactions conserved among Saccharomyces cerevisiae, Homo sapiens, Arabidopsis thaliana, and Drosophila melanogaster were found to be associated with RNA splicing. In addition, overlap of predicted PPIs in reference organisms, Gene Ontology, and topology models in S. mimosarum are also reported. CONCLUSIONS Addition of Stegodyphus, a spider representative of interactomic research, provides the possibility of obtaining deeper insights into the evolution of PPI networks among different animal species. This work largely supports the utility of the "stratus clouds" model for predicted PPIs, providing a roadmap for integrative systems biology in S. mimosarum.
Collapse
Affiliation(s)
- Xiu Wang
- Institute of Ecology, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, ZJ310058, People's Republic of China.,Institute of Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, ZJ310058, People's Republic of China
| | - Yongfeng Jin
- Institute of Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, ZJ310058, People's Republic of China.
| |
Collapse
|