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Manipur I, Giordano M, Piccirillo M, Parashuraman S, Maddalena L. Community Detection in Protein-Protein Interaction Networks and Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:217-237. [PMID: 34951849 DOI: 10.1109/tcbb.2021.3138142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.
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Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010076. [PMID: 36676027 PMCID: PMC9861397 DOI: 10.3390/life13010076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022]
Abstract
Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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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]
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Perlo V, Margarido GRA, Botha FC, Furtado A, Hodgson-Kratky K, Correr FH, Henry RJ. Transcriptome changes in the developing sugarcane culm associated with high yield and early-season high sugar content. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1619-1636. [PMID: 35224663 PMCID: PMC9110458 DOI: 10.1007/s00122-022-04058-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Sugarcane, with its exceptional carbon dioxide assimilation, biomass and sugar yield, has a high potential for the production of bio-energy, bio-plastics and high-value products in the food and pharmaceutical industries. A crucial challenge for long-term economic viability and environmental sustainability is also to optimize the production of biomass composition and carbon sequestration. Sugarcane varieties such as KQ228 and Q253 are highly utilized in the industry. These varieties are characterized by a high early-season sugar content associated with high yield. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 120 samples in triplicate across 24 sugarcane cultivars at five different development stages. Weighted gene co-expression network analysis (WGCNA) was used and revealed for the first time two sets of co-expressed genes with a distinct and opposite correlation between fibre and sugar content. Gene identification and metabolism pathways analysis was used to define these two sets of genes. Correlation analysis identified a large number of interconnected metabolic pathways linked to sugar content and fibre content. Unsupervised hierarchical clustering of gene expression revealed a stronger level of segregation associated with the genotypes than the stage of development, suggesting a dominant genetic influence on biomass composition and facilitating breeding selection. Characterization of these two groups of co-expressed key genes can help to improve breeding program for high fibre, high sugar species or plant synthetic biology.
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Affiliation(s)
- Virginie Perlo
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Gabriel R. A. Margarido
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, São Paulo, 13418-900 Brazil
| | - Frederik C. Botha
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Agnelo Furtado
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Katrina Hodgson-Kratky
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Fernando H. Correr
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, São Paulo, 13418-900 Brazil
| | - Robert J. Henry
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
- The University of Queensland, Level 2, Queensland Bioscience Precinct [#80], 306 Carmody Road St Lucia, St Lucia, QLD 4072 Australia
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Integrated Proteomic and Transcriptomic Analysis of Gonads Reveal Disruption of Germ Cell Proliferation and Division, and Energy Storage in Glycogen in Sterile Triploid Pacific Oysters ( Crassostrea gigas). Cells 2021; 10:cells10102668. [PMID: 34685648 PMCID: PMC8534442 DOI: 10.3390/cells10102668] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/24/2021] [Accepted: 09/29/2021] [Indexed: 12/19/2022] Open
Abstract
Triploid oysters have poor gonadal development, which can not only bring higher economic benefits but also have a potential application in the genetic containment of aquaculture. However, the key factors that influence germ cell development in triploid oysters remain unclear. In this study, data-independent acquisition coupled to transcriptomics was applied to identify genes/proteins related to sterility in triploid Crassostrea gigas. Eighty-four genes were differentially expressed at both the protein and mRNA levels between fertile and sterile females. For male oysters, 207 genes were differentially expressed in the transcriptomic and proteomic analysis. A large proportion of downregulated genes were related to cell division, which may hinder germ cell proliferation and cause apoptosis. In sterile triploid females, a primary cause of sterility may be downregulation in the expression levels of certain mitotic cell cycle-related genes. In sterile triploid males, downregulation of genes related to cell cycle and sperm motility indicated that the disruption of mitosis or meiosis and flagella defects may be linked with the blocking of spermatogenesis. Additionally, the genes upregulated in sterile oysters were mainly associated with the biosynthesis of glycogen and fat, suggesting that sterility in triploids stimulates the synthesis of glycogen and energy conservation in gonad tissue.
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Pournoor E, Mousavian Z, Nowzari-Dalini A, Masoudi-Nejad A. A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma. PLoS One 2021; 16:e0255718. [PMID: 34370784 PMCID: PMC8351981 DOI: 10.1371/journal.pone.0255718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/22/2021] [Indexed: 11/19/2022] Open
Abstract
Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Abbas Nowzari-Dalini
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Mizuno T, Morita K, Kusuhara H. Interesting Properties of Profile Data Analysis in the Understanding and Utilization of the Effects of Drugs. Biol Pharm Bull 2020; 43:1435-1442. [DOI: 10.1248/bpb.b20-00301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, the University of Tokyo
| | - Katsuhisa Morita
- Graduate School of Pharmaceutical Sciences, the University of Tokyo
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