1
|
Lawson S, Donovan D, Lefevre J. An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork. PLoS One 2024; 19:e0311433. [PMID: 39361678 DOI: 10.1371/journal.pone.0311433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Accepted: 09/12/2024] [Indexed: 10/05/2024] Open
Abstract
The use of graph centrality measures applied to biological networks, such as protein interaction networks, underpins much research into identifying key players within biological processes. This approach however is restricted to dyadic interactions and it is well-known that in many instances interactions are polyadic. In this study we illustrate the merit of using hypergraph centrality applied to a hypernetwork as an alternative. Specifically, we review and propose an extension to a recently introduced node and edge nonlinear hypergraph centrality model which provides mutually dependent node and edge centralities. A Saccharomyces Cerevisiae protein complex hypernetwork is used as an example application with nodes representing proteins and hyperedges representing protein complexes. The resulting rankings of the nodes and edges are considered to see if they provide insight into the essentiality of the proteins and complexes. We find that certain variations of the model predict essentiality more accurately and that the degree-based variation illustrates that the centrality-lethality rule extends to a hypergraph setting. In particular, through exploitation of the models flexibility, we identify small sets of proteins densely populated with essential proteins. One of the key advantages of applying this model to a protein complex hypernetwork is that it also provides a classification method for protein complexes, unlike previous approaches which are only concerned with classifying proteins.
Collapse
Affiliation(s)
- Sarah Lawson
- ARC Centre of Excellence, Plant Success in Nature and Agriculture, School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, Australia
| | - Diane Donovan
- ARC Centre of Excellence, Plant Success in Nature and Agriculture, School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, Australia
| | - James Lefevre
- ARC Centre of Excellence, Plant Success in Nature and Agriculture, School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, Australia
| |
Collapse
|
2
|
Asadi R, Shadpour P, Nakhaei A. Non-dialyzable uremic toxins and renal tubular cell damage in CKD patients: a systems biology approach. Eur J Med Res 2024; 29:412. [PMID: 39123228 PMCID: PMC11311939 DOI: 10.1186/s40001-024-01951-z] [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: 08/02/2023] [Accepted: 06/25/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Chronic kidney disease presents global health challenges, with hemodialysis as a common treatment. However, non-dialyzable uremic toxins demand further investigation for new therapeutic approaches. Renal tubular cells require scrutiny due to their vulnerability to uremic toxins. METHODS In this study, a systems biology approach utilized transcriptomics data from healthy renal tubular cells exposed to healthy and post-dialysis uremic plasma. RESULTS Differential gene expression analysis identified 983 up-regulated genes, including 70 essential proteins in the protein-protein interaction network. Modularity-based clustering revealed six clusters of essential proteins associated with 11 pathological pathways activated in response to non-dialyzable uremic toxins. CONCLUSIONS Notably, WNT1/11, AGT, FGF4/17/22, LMX1B, GATA4, and CXCL12 emerged as promising targets for further exploration in renal tubular pathology related to non-dialyzable uremic toxins. Understanding the molecular players and pathways linked to renal tubular dysfunction opens avenues for novel therapeutic interventions and improved clinical management of chronic kidney disease and its complications.
Collapse
Affiliation(s)
- Roya Asadi
- Industrial Engineering Department, Faculty of Technical and Engineering, University of Science and Culture (USC), Tehran, Iran
| | - Pejman Shadpour
- Hospital Management Research Center (HMRC), Hasheminejad Kidney Center (HKC), Iran University of Medical Sciences (IUMS), Tehran, Iran.
| | - Akram Nakhaei
- Computer Engineering Department, Mazandaran University of Science and Technology (MUST), Babol, Iran.
| |
Collapse
|
3
|
Ye C, Wu Q, Chen S, Zhang X, Xu W, Wu Y, Zhang Y, Yue Y. ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization. BMC Genomics 2024; 25:117. [PMID: 38279081 PMCID: PMC10821549 DOI: 10.1186/s12864-024-10019-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
BACKGROUND In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species. RESULTS We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset. CONCLUSIONS Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.
Collapse
Affiliation(s)
- Chen Ye
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China
| | - Qi Wu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China
| | - Shuxia Chen
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China
| | - Xuemei Zhang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China
| | - Wenwen Xu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China
| | - Yunzhi Wu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China
| | - Youhua Zhang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China
| | - Yi Yue
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China.
- Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, 230036, China.
| |
Collapse
|
4
|
Rao A, Gollapalli P, Shetty NP. Gene expression profile analysis unravelled the systems level association of renal cell carcinoma with diabetic nephropathy and Matrix-metalloproteinase-9 as a potential therapeutic target. J Biomol Struct Dyn 2023; 41:7535-7550. [PMID: 36106961 DOI: 10.1080/07391102.2022.2122567] [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: 01/25/2022] [Accepted: 09/03/2022] [Indexed: 10/14/2022]
Abstract
Type 2 diabetes (T2D) and cancer share many common risk factors. However, the potential biological link that connects the two at the molecular level is still unclear. The experimental evidence suggests that several genes and their pathways may be involved in developing cancerous conditions associated with diabetes. In this study, we identified the protein-protein interaction (PPI) networks and the hub protein(s) that interlink T2D and cancer using genome-scale differential gene expression profiles. Further, the PPI network of AMP-activated protein kinase (AMPK) in cancer was analyzed to explore novel insights into the molecular association between the two conditions. The densely connected regions were analyzed by constructing the backbone and subnetworks with key nodes and shortest pathways, respectively. The PPI network studies identified Matrix-metalloproteinase-9 (MMP-9) as a hub protein playing a vital role in glomerulonephritis tubular diseases and some genetic kidney diseases. MMP-9 was also associated with different growth factors, like tumor necrosis factor (TNF-α), transforming growth factor 1 (TGF-1), and pathways like chemokine signaling, NOD-like receptor signaling, etc. Further, the molecular docking and molecular dynamic simulation studies supported the druggability of MMP-9, suggesting it as a potential therapeutic target in treating renal cell carcinoma linked with diabetic kidney disease.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Aditya Rao
- Plant Cell Biotechnology Department, CSIR-Central Food Technological Research Institute, Mysore, Karnataka, India
| | - Pavan Gollapalli
- Center for Bioinformatics and Biostatistics, Nitte (Deemed to be University), Mangalore, Karnataka, India
| | - Nandini Prasad Shetty
- Plant Cell Biotechnology Department, CSIR-Central Food Technological Research Institute, Mysore, Karnataka, India
| |
Collapse
|
5
|
Bhattacharya R, Nagwani NK, Tripathi S. Detecting influential nodes with topological structure via Graph Neural Network approach in social networks. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2023; 15:2233-2246. [PMID: 37256031 PMCID: PMC10163927 DOI: 10.1007/s41870-023-01271-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 04/05/2023] [Indexed: 06/01/2023]
Abstract
Detecting influential nodes in complex social networks is crucial due to the enormous amount of data and the constantly changing behavior of existing topologies. Centrality-based and machine-learning approaches focus mostly on node topologies or feature values in their evaluation of nodes' relevance. However, both network topologies and node attributes should be taken into account when determining the influential value of nodes. This research has proposed a deep learning model called Graph Convolutional Networks (GCN) to discover the significant nodes in graph-based large datasets. A deep learning framework for identifying influential nodes with structural centrality via Graph Convolutional Networks called DeepInfNode has been developed. The proposed approach measures up contextual information from Susceptible-Infected-Recovered (SIR) model trials to measure the rate of infection to develop node representations. In the experimental section, acquired experimental results indicate that the suggested model has a higher F1 and Area under the curve (AUC) value. The findings indicate that the strategy is both effective and precise in terms of suggesting new linkages. The proposed DeepInfNode model outperforms state-of-the-art approaches on a variety of publicly available standard graph datasets, achieving an increase in performance of up to 99.1% of accuracy.
Collapse
Affiliation(s)
- Riju Bhattacharya
- Department of Computer Science and Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh 492010 India
| | - Naresh Kumar Nagwani
- Department of Computer Science and Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh 492010 India
| | - Sarsij Tripathi
- Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Prayagraj, Uttar Pradesh 211004 India
| |
Collapse
|
6
|
Kou J, Jia P, Liu J, Dai J, Luo H. Identify Influential Nodes in Social Networks with Graph Multi-head Attention Regression Model. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
7
|
Khojasteh H, Khanteymoori A, Olyaee MH. Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features. Sci Rep 2022; 12:5867. [PMID: 35393450 PMCID: PMC8988119 DOI: 10.1038/s41598-022-08574-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 03/03/2022] [Indexed: 01/04/2023] Open
Abstract
SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.
Collapse
Affiliation(s)
- Hakimeh Khojasteh
- Department of Computer Engineering, University of Zanjan, Zanjan, Iran
| | | | - Mohammad Hossein Olyaee
- Department of Computer Engineering, Engineering Faculty, University of Gonabad, Zanjan, Gonabad, Iran
| |
Collapse
|
8
|
Asabere NY, Fiamavle E, Agyiri J, Torgby WK, Dzata JE, Doe NP. SARCP. INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2022. [DOI: 10.4018/ijdsst.286691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the domain of cyber security, the defence mechanisms of networks has traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that it is vital that they maneuver such attacks before the network is totally compromised. In this paper, we utilize the Betweenness Centrality network measure (social property) to discover possible cyber-attack paths and then employ computation of similar personality of nodes/users to generate predictions about possible attacks within the network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP), as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed method is favorable and effective.
Collapse
|
9
|
Li J, Zeng X, Yang X, Ding H. Lycopene ameliorates skin aging by regulating the insulin resistance pathway and activating SIRT1. Food Funct 2022; 13:11307-11320. [DOI: 10.1039/d2fo01111e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Lycopene could reverse insulin resistance through SIRT1 during skin aging and promotes microcirculation via the improvement of microvascular neovascularization to protect aging skin.
Collapse
Affiliation(s)
- Jing Li
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan, P. R. China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430000, Hubei, P. R. China
| | - Xin Zeng
- Nanchong Key Laboratory of Individualized Drug Therapy, Department of Pharmacy, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430000, Hubei, P. R. China
| | - Xiaolong Yang
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan, P. R. China
| | - Hong Ding
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430000, Hubei, P. R. China
| |
Collapse
|
10
|
Hafsa U, Chuwdhury GS, Hasan MK, Ahsan T, Moni MA. An in silico approach towards identification of novel drug targets in Klebsiella oxytoca. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
|
11
|
Gollapalli P, Selvan G T, H M, Shetty P, Kumari N S. Genome-scale protein interaction network construction and topology analysis of functional hypothetical proteins in Helicobacter pylori divulges novel therapeutic targets. Microb Pathog 2021; 161:105293. [PMID: 34800634 DOI: 10.1016/j.micpath.2021.105293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/25/2021] [Accepted: 11/12/2021] [Indexed: 02/07/2023]
Abstract
The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.
Collapse
Affiliation(s)
- Pavan Gollapalli
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India.
| | - Tamizh Selvan G
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| | - Manjunatha H
- Department of Biochemistry, Jnana Bharathi Campus, Bangalore University, Bangalore, Karnataka, 560056, India
| | - Praveenkumar Shetty
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| | - Suchetha Kumari N
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| |
Collapse
|
12
|
Campos TL, Korhonen PK, Hofmann A, Gasser RB, Young ND. Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications. Biotechnol Adv 2021; 54:107822. [PMID: 34461202 DOI: 10.1016/j.biotechadv.2021.107822] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 08/17/2021] [Accepted: 08/24/2021] [Indexed: 12/17/2022]
Abstract
The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.
Collapse
Affiliation(s)
- Tulio L Campos
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia; Bioinformatics Core Facility, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz (IAM-Fiocruz), Recife, Pernambuco, Brazil
| | - Pasi K Korhonen
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Andreas Hofmann
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
| | - Neil D Young
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
| |
Collapse
|
13
|
Chiliński M, Sengupta K, Plewczynski D. From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect. Semin Cell Dev Biol 2021; 121:171-185. [PMID: 34429265 DOI: 10.1016/j.semcdb.2021.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 08/06/2021] [Accepted: 08/12/2021] [Indexed: 12/30/2022]
Abstract
The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.
Collapse
Affiliation(s)
- Mateusz Chiliński
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland; Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Kaustav Sengupta
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Dariusz Plewczynski
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland; Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland.
| |
Collapse
|
14
|
He X, Kuang L, Chen Z, Tan Y, Wang L. Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network. Front Genet 2021; 12:708162. [PMID: 34267785 PMCID: PMC8276041 DOI: 10.3389/fgene.2021.708162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 05/31/2021] [Indexed: 11/21/2022] Open
Abstract
In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.
Collapse
Affiliation(s)
- Xin He
- College of Computer, Xiangtan University, Xiangtan, China
| | - Linai Kuang
- College of Computer, Xiangtan University, Xiangtan, China
| | - Zhiping Chen
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, China
| | - Yihong Tan
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, China
| | - Lei Wang
- College of Computer, Xiangtan University, Xiangtan, China
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, China
| |
Collapse
|
15
|
Feng Wang, Jiang W, Chen B, Li R. The Mechanism of MSX1 and PAX9 Implication in Tooth Development Based on the Weighted Gene Co-Expression Network Analysis. Russ J Dev Biol 2021. [DOI: 10.1134/s1062360421030085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
16
|
Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets. Microb Pathog 2021; 158:105059. [PMID: 34157412 DOI: 10.1016/j.micpath.2021.105059] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/09/2021] [Accepted: 06/14/2021] [Indexed: 11/24/2022]
Abstract
The Helicobacter pylori chronic colonization produces a wide range of gastric diseases in the gastric mucosa by abetting inflammation. Amidst coevolution and reorganization of its metabolism with humans, it has become difficult still imperative to understand and prevent its growth. This study focus to explore functional insights into identification of hub proteins/genes by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We have constructed a PPI network of 123 essential genes along with 1213 interactions in H. pylori 26695. The degree and other centrality measures analysis assist in identifying the important hub nodes, which are top-ranked proteins. A total of nine proteins (recA, guaA, dnaK, rpsB, rplQ, rpmA, rpmC, rpmF, and rpsE) were obtained with high degree (k), betweenness centrality (BC) value. Gene ontology analysis reveals 8, 5 and 3 GO terms correspond to biological processes, cellular components and molecular function respectively. Gene complexes of hypothetical proteins (HPs) were related to aminoacyl-tRNA biosynthesis, biosynthesis of secondary metabolites, bacterial secretion system and protein export. The MCODE analysis revealed that protein from module M1, M3 and M6 include the proteins which have highest degree and BC values. It is noteworthy to mention that the bifunctional GMP synthase/glutamine amidotransferase protein (guaA), molecular chaperon (dnaK), recombinase A (recA) constitute as hub proteins. As a result, these genes are considered as network hub nodes that might be used as therapeutic targets. Our analysis affords a detailed understanding of the molecular process and pathways regulated by the essential genes in H. pylori 26695.
Collapse
|
17
|
Liu Z, Ma A, Mathé E, Merling M, Ma Q, Liu B. Network analyses in microbiome based on high-throughput multi-omics data. Brief Bioinform 2021; 22:1639-1655. [PMID: 32047891 PMCID: PMC7986608 DOI: 10.1093/bib/bbaa005] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 01/07/2020] [Accepted: 01/08/2020] [Indexed: 02/06/2023] Open
Abstract
Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.
Collapse
Affiliation(s)
- Zhaoqian Liu
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Marlena Merling
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
18
|
Wang YXR, Li L, Li JJ, Huang H. Network Modeling in Biology: Statistical Methods for Gene and Brain Networks. Stat Sci 2021; 36:89-108. [PMID: 34305304 PMCID: PMC8296984 DOI: 10.1214/20-sts792] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using covariates as a first step. We provide a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks.
Collapse
Affiliation(s)
- Y X Rachel Wang
- School of Mathematics and Statistics, University of Sydney, Australia
| | - Lexin Li
- Department of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley
| | | | - Haiyan Huang
- Department of Statistics, University of California, Berkeley
| |
Collapse
|
19
|
Abstract
Background:
The basic building block of a body is protein which is a complex system
whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore,
careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug
designing. Protein structures are described at their different levels of complexity: primary (chain),
secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of
protein is a difficult task but it can be analyzed as a network of interconnection between its
component, where amino acids are considered as nodes and interconnection between them are
edges.
Objective:
Many literature works have proven that the small world network concept provides
many new opportunities to investigate network of biological systems. The objective of this paper is
analyzing the protein structure using small world concept.
Methods:
Protein is analyzed using small world network concept, specifically where extreme
condition is having a degree distribution which follows power law. For the correct verification of
the proposed approach, dataset of the Oncogene protein structure is analyzed using Python
programming.
Results:
Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG))
using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree
distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are
calculated for 1323 nodes and graphs are plotted.
Conclusion:
Ultimately, it is concluded that there exist hubs with higher centrality degree but less
in number, and they are expected to be robust toward harmful effects of mutations with new
functions.
Collapse
Affiliation(s)
- Neetu Kumari
- Department of Computer Science, Banaras Hindu University, Varanasi, India
| | - Anshul Verma
- Department of Computer Science, Banaras Hindu University, Varanasi, India
| |
Collapse
|
20
|
Klimm F, Toledo EM, Monfeuga T, Zhang F, Deane CM, Reinert G. Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks. BMC Genomics 2020; 21:756. [PMID: 33138772 PMCID: PMC7607865 DOI: 10.1186/s12864-020-07144-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 10/12/2020] [Indexed: 12/14/2022] Open
Abstract
Background Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. Results In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. Conclusions The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues. Supplementary Information The online version contains supplementary material available at (doi:10.1186/s12864-020-07144-2).
Collapse
Affiliation(s)
- Florian Klimm
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK. .,Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, UK.
| | - Enrique M Toledo
- Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, OX3 7FZ, UK
| | - Thomas Monfeuga
- Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, OX3 7FZ, UK
| | - Fang Zhang
- Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, OX3 7FZ, UK
| | | | - Gesine Reinert
- Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| |
Collapse
|
21
|
Zhao G, Jia P, Zhou A, Zhang B. InfGCN: Identifying influential nodes in complex networks with graph convolutional networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
22
|
Singh P, Sharma A, Jha R, Arora S, Ahmad R, Rahmani AH, Almatroodi SA, Dohare R, Syed MA. Transcriptomic analysis delineates potential signature genes and miRNAs associated with the pathogenesis of asthma. Sci Rep 2020; 10:13354. [PMID: 32770056 PMCID: PMC7414199 DOI: 10.1038/s41598-020-70368-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 07/22/2020] [Indexed: 12/21/2022] Open
Abstract
Asthma is a multifarious disease affecting several million people around the world. It has a heterogeneous risk architecture inclusive of both genetic and environmental factors. This heterogeneity can be utilised to identify differentially expressed biomarkers of the disease, which may ultimately aid in the development of more localized and molecularly targeted therapies. In this respect, our study complies with meta-analysis of microarray datasets containing mRNA expression profiles of both asthmatic and control patients, to identify the critical Differentially Expressed Genes (DEGs) involved in the pathogenesis of asthma. We found a total of 30 DEGs out of which 13 were involved in the pathway and functional enrichment analysis. Moreover, 5 DEGs were identified as the hub genes by network centrality-based analysis. Most hub genes were involved in protease/antiprotease pathways. Also, 26 miRNAs and 20 TFs having an association with these hub genes were found to be intricated in a 3-node miRNA Feed-Forward Loop. Out of these, miR-34b and miR-449c were identified as the key miRNAs regulating the expression of SERPINB2 gene and SMAD4 transcription factor. Thus, our study is suggestive of certain miRNAs and unexplored pathways which may pave a way to unravel critical therapeutic targets in asthma.
Collapse
Affiliation(s)
- Prithvi Singh
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Archana Sharma
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Rishabh Jha
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Shweta Arora
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Rafiq Ahmad
- Centre for Nanoscience and Nanotechnology, Jamia Millia Islamia, New Delhi, 110025, India
| | - Arshad Husain Rahmani
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia
| | - Saleh A Almatroodi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia
| | - Ravins Dohare
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
| | - Mansoor Ali Syed
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
| |
Collapse
|
23
|
Uncovering the anticancer mechanism of petroleum extracts of Farfarae Flos against Lewis lung cancer by metabolomics and network pharmacology analysis. Biomed Chromatogr 2020; 34:e4878. [DOI: 10.1002/bmc.4878] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/30/2020] [Accepted: 05/06/2020] [Indexed: 01/19/2023]
|
24
|
Mao Y, Chen L, Li J, Shangguan AJ, Kujawa S, Zhao H. A network analysis revealed the essential and common downstream proteins related to inguinal hernia. PLoS One 2020; 15:e0226885. [PMID: 31910207 PMCID: PMC6946160 DOI: 10.1371/journal.pone.0226885] [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: 08/07/2019] [Accepted: 12/08/2019] [Indexed: 01/10/2023] Open
Abstract
Although more than 1 in 4 men develop symptomatic inguinal hernia during their lifetime, the molecular mechanism behind inguinal hernia remains unknown. Here, we explored the protein-protein interaction network built on known inguinal hernia-causative genes to identify essential and common downstream proteins for inguinal hernia formation. We discovered that PIK3R1, PTPN11, TGFBR1, CDC42, SOS1, and KRAS were the most essential inguinal hernia-causative proteins and UBC, GRB2, CTNNB1, HSP90AA1, CBL, PLCG1, and CRK were listed as the most commonly-involved downstream proteins. In addition, the transmembrane receptor protein tyrosine kinase signaling pathway was the most frequently found inguinal hernia-related pathway. Our in silico approach was able to uncover a novel molecular mechanism underlying inguinal hernia formation by identifying inguinal hernia-related essential proteins and potential common downstream proteins of inguinal hernia-causative proteins.
Collapse
Affiliation(s)
- Yimin Mao
- School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
- Applied Science Institute, Jiangxi University of Science and Technology, Jiangxi, China
| | - Le Chen
- School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
| | - Jianghua Li
- School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
| | - Anna Junjie Shangguan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Stacy Kujawa
- Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Hong Zhao
- Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
- * E-mail:
| |
Collapse
|
25
|
Abstract
Background:
Essential proteins play important roles in the survival or reproduction of
an organism and support the stability of the system. Essential proteins are the minimum set of
proteins absolutely required to maintain a living cell. The identification of essential proteins is a
very important topic not only for a better comprehension of the minimal requirements for cellular
life, but also for a more efficient discovery of the human disease genes and drug targets.
Traditionally, as the experimental identification of essential proteins is complex, it usually requires
great time and expense. With the cumulation of high-throughput experimental data, many
computational methods that make useful complements to experimental methods have been
proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify
essential proteins is of great significance for discovering disease genes and drug design, and has
great potential for applications in basic and synthetic biology research.
Objective:
The aim of this paper is to provide a review on the identification of essential proteins
and genes focusing on the current developments of different types of computational methods, point
out some progress and limitations of existing methods, and the challenges and directions for
further research are discussed.
Collapse
Affiliation(s)
- Ming Fang
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Ling Guo
- College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China
| |
Collapse
|
26
|
Xu B, Guan J, Wang Y, Wang Z. Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:377-387. [PMID: 28504946 DOI: 10.1109/tcbb.2017.2701824] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Essential proteins are critical to the development and survival of cells. Identification of essential proteins is helpful for understanding the minimal set of required genes in a living cell and for designing new drugs. To detect essential proteins, various computational methods have been proposed based on protein-protein interaction (PPI) networks. However, protein interaction data obtained by high-throughput experiments usually contain high false positives, which negatively impacts the accuracy of essential protein detection. Moreover, most existing studies focused on the local information of proteins in PPI networks, while ignoring the influence of indirect protein interactions on essentiality. In this paper, we propose a novel method, called Essentiality Ranking (EssRank in short), to boost the accuracy of essential protein detection. To deal with the inaccuracy of PPI data, confidence scores of interactions are evaluated by integrating various biological information. Weighted edge clustering coefficient (WECC), considering both interaction confidence scores and network topology, is proposed to calculate edge weights in PPI networks. The weight of each node is evaluated by the sum of WECC values of its linking edges. A random walk method, making use of both direct and indirect protein interactions, is then employed to calculate protein essentiality iteratively. Experimental results on the yeast PPI network show that EssRank outperforms most existing methods, including the most commonly-used centrality measures (SC, DC, BC, CC, IC, and EC), topology based methods (DMNC and NC) and the data integrating method IEW.
Collapse
|
27
|
Liu X, Hong Z, Liu J, Lin Y, Rodríguez-Patón A, Zou Q, Zeng X. Computational methods for identifying the critical nodes in biological networks. Brief Bioinform 2019; 21:486-497. [DOI: 10.1093/bib/bbz011] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 12/03/2018] [Accepted: 01/11/2019] [Indexed: 12/28/2022] Open
Abstract
Abstract
A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.
Collapse
Affiliation(s)
- Xiangrong Liu
- Department of Computer Science, Xiamen University, China
| | - Zengyan Hong
- Department of Computer Science, Xiamen University, China
| | - Juan Liu
- Department of Computer Science, Xiamen University, China
| | - Yuan Lin
- ITOP Section, DNB Bank ASA, Solheimsgaten, Bergen, Norway
| | - Alfonso Rodríguez-Patón
- Universidad Politécnica de Madrid (UPM) Campus Montegancedo s/n, Boadilla del Monte, Madrid, Spain
| | - Quan Zou
- Department of Computer Science, Xiamen University, China
- Insitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, China
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | | |
Collapse
|
28
|
Zhang F, Peng W, Yang Y, Dai W, Song J. A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein⁻Protein Interactive Networks. Genes (Basel) 2019; 10:genes10010031. [PMID: 30626157 PMCID: PMC6356314 DOI: 10.3390/genes10010031] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 12/24/2018] [Accepted: 01/02/2019] [Indexed: 11/16/2022] Open
Abstract
Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein⁻protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks' similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.
Collapse
Affiliation(s)
- Fengyu Zhang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
- Computer Center of Kunming University of Science and Technology, Kunming 650093, China.
| | - Yunfei Yang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
| | - Wei Dai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
| | - Junrong Song
- Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China.
| |
Collapse
|
29
|
Dong C, Jin YT, Hua HL, Wen QF, Luo S, Zheng WX, Guo FB. Comprehensive review of the identification of essential genes using computational methods: focusing on feature implementation and assessment. Brief Bioinform 2018; 21:171-181. [PMID: 30496347 DOI: 10.1093/bib/bby116] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 11/01/2018] [Accepted: 11/02/2018] [Indexed: 02/06/2023] Open
Abstract
Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level. We have described how to implement the useful forms of these features and evaluated their performance based on the data of Escherichia coli MG1655, Bacillus subtilis 168 and human. The prerequisite and applicable range of these features is described. In addition, we have investigated the techniques used to weight features in various models. To facilitate researchers in the field, two available online tools, which are accessible for free and can be directly used to predict gene essentiality in prokaryotes and humans, were referred. This article provides a simple guide for the identification of essential genes in prokaryotes and eukaryotes.
Collapse
Affiliation(s)
- Chuan Dong
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan-Ting Jin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong-Li Hua
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing-Feng Wen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Sen Luo
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wen-Xin Zheng
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Feng-Biao Guo
- School of Life Science and Technology, Center for Informational Biology, Intelligent Learning Institute for Science and Application, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
30
|
Arasteh M, Alizadeh S. A fast divisive community detection algorithm based on edge degree betweenness centrality. APPL INTELL 2018. [DOI: 10.1007/s10489-018-1297-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
31
|
A systematic survey of centrality measures for protein-protein interaction networks. BMC SYSTEMS BIOLOGY 2018; 12:80. [PMID: 30064421 PMCID: PMC6069823 DOI: 10.1186/s12918-018-0598-2] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 06/22/2018] [Indexed: 12/12/2022]
Abstract
Background Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. Results We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. Conclusions The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node. Electronic supplementary material The online version of this article (10.1186/s12918-018-0598-2) contains supplementary material, which is available to authorized users.
Collapse
|
32
|
Guo Y, Gao W, Wang D, Liu W, Liu Z. Gene alterations in monocytes are pathogenic factors for immunoglobulin a nephropathy by bioinformatics analysis of microarray data. BMC Nephrol 2018; 19:184. [PMID: 30029622 PMCID: PMC6053766 DOI: 10.1186/s12882-018-0944-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 06/07/2018] [Indexed: 11/27/2022] Open
Abstract
Background Immunoglobulin A nephropathy (IgAN) is the most frequent primary glomerulopathy worldwide. The study aimed to provide potential molecular biomarkers for IgAN management. Methods The public gene expression profiling GSE58539 was utilized, which contained 17 monocytes samples (8 monocytes samples isolated from IgAN patients and 9 monocytes samples isolated from healthy blood donors). Firstly, differentially expressed genes (DEGs) between the two kinds of samples were identified by limma package. Afterwards, pathway enrichment analysis was implemented. Thereafter, protein-protein interaction (PPI) network was constructed and key nodes in PPI network were predicted using four network centrality analyses. Ultimately, gene functional interaction (FI) was constructed according to expressions in each sample, and then module network was extracted from FI network. Results A total of 678 DEGs were screened out, of these, 72 DEGs were identified as crucial nodes in PPI network that could well distinguish IgAN and healthy samples. In particular, IL6, TNF, IL1B, PRKACA and CCL20 were closely related to pathways such as hematopoietic cell lineage, apoptosis and Toll-like receptor (TLR) signaling pathway. Moreover, 12 genes in the FI network belonged to the 72 identified key nodes, such as CCL20, HDAC10, FPR2 and PRKACA, which were also key genes in 4 module networks. Conclusions Several crucial genes were identified in monocytes of IgAN patients, such as IL6, TNF, IL1B, CCL20, PRKACA, FPR2 and HDAC10. These genes might co-involve in pathways such as TLR and apoptosis signaling during IgAN progression.
Collapse
Affiliation(s)
- Yingbo Guo
- Department of Nephropathy, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Beijng, 100078, China
| | - Wenfeng Gao
- Department of Urology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijng, 100700, China
| | - Danyang Wang
- Department of Nephropathy and Endocrinology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, No. 5 Haiyuncang, Dongcheng District, Beijng City, 100700, China
| | - Weijing Liu
- Department of Nephropathy and Endocrinology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, No. 5 Haiyuncang, Dongcheng District, Beijng City, 100700, China
| | - Zhongjie Liu
- Department of Nephropathy and Endocrinology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, No. 5 Haiyuncang, Dongcheng District, Beijng City, 100700, China.
| |
Collapse
|
33
|
Feature Selection via Swarm Intelligence for Determining Protein Essentiality. MOLECULES (BASEL, SWITZERLAND) 2018; 23:molecules23071569. [PMID: 29958434 PMCID: PMC6100311 DOI: 10.3390/molecules23071569] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/22/2018] [Accepted: 06/25/2018] [Indexed: 01/24/2023]
Abstract
Protein essentiality is fundamental to comprehend the function and evolution of genes. The prediction of protein essentiality is pivotal in identifying disease genes and potential drug targets. Since the experimental methods need many investments in time and funds, it is of great value to predict protein essentiality with high accuracy using computational methods. In this study, we present a novel feature selection named Elite Search mechanism-based Flower Pollination Algorithm (ESFPA) to determine protein essentiality. Unlike other protein essentiality prediction methods, ESFPA uses an improved swarm intelligence⁻based algorithm for feature selection and selects optimal features for protein essentiality prediction. The first step is to collect numerous features with the highly predictive characteristics of essentiality. The second step is to develop a feature selection strategy based on a swarm intelligence algorithm to obtain the optimal feature subset. Furthermore, an elite search mechanism is adopted to further improve the quality of feature subset. Subsequently a hybrid classifier is applied to evaluate the essentiality for each protein. Finally, the experimental results show that our method is competitive to some well-known feature selection methods. The proposed method aims to provide a new perspective for protein essentiality determination.
Collapse
|
34
|
Zeng W, Rao N, Li Q, Wang G, Liu D, Li Z, Yang Y. Genome-wide Analyses on Single Disease Samples for Potential Biomarkers and Biological Features of Molecular Subtypes: A Case Study in Gastric Cancer. Int J Biol Sci 2018; 14:833-842. [PMID: 29989098 PMCID: PMC6036754 DOI: 10.7150/ijbs.24816] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 03/06/2018] [Indexed: 02/06/2023] Open
Abstract
Purpose: Based on the previous 3 well-defined subtypes of gastric adenocarcinoma (invasive, proliferative and metabolic), we aimed to find potential biomarkers and biological features of each subtype. Methods: The genome-wide co-expression network of each subtype of gastric cancer was firstly constructed. Then, the functional modules in each genome-wide co-expression network were divided. Next, the key genes were screened from each functional module. Finally, the enrichment analysis was performed on the key genes to mine the biological features of each subtype. Comparative analysis between each pair of subtypes was performed to find the common and unique features among different subtypes. Results: A total of 207 key genes were identified in invasive, 215 key genes in proliferative, and 204 key genes in metabolic subtypes. Most key genes in each subtype were unique and new findings compared with that of the existing related researches. The GO and KEGG enrichment analyses for the key genes of each subtype revealed important biological features of each subtype. Conclusions: For a subtype, most identified key genes and important biological features were unique, which means that the key genes can be used as the potential biomarker of a subtype, and each subtype of gastric cancer might have different occurrence and development mechanisms. Thus, different diagnosis and therapy methods should be applied to the invasive, proliferative and metabolic subtypes of gastric cancer.
Collapse
Affiliation(s)
- Wei Zeng
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China.,Department of Biomedical Engineering, School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, 643000, China
| | - Nini Rao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China.,Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, 523808, China
| | - Qian Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Guangbin Wang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dingyun Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhengwen Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuntao Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
35
|
Li M, Li W, Wu FX, Pan Y, Wang J. Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information. J Theor Biol 2018; 447:65-73. [PMID: 29571709 DOI: 10.1016/j.jtbi.2018.03.029] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 03/19/2018] [Accepted: 03/20/2018] [Indexed: 01/07/2023]
Abstract
Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC.
Collapse
Affiliation(s)
- Min Li
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Wenkai Li
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA.
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| |
Collapse
|
36
|
Quevedo-Tumailli VF, Ortega-Tenezaca B, González-Díaz H. Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome. J Proteome Res 2018; 17:1258-1268. [DOI: 10.1021/acs.jproteome.7b00861] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Viviana F. Quevedo-Tumailli
- RNASA-IMEDIR,
Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain
- Universidad Estatal Amazónica UEA, Puyo, Pastaza, Ecuador
| | - Bernabé Ortega-Tenezaca
- RNASA-IMEDIR,
Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain
- Universidad Estatal Amazónica UEA, Puyo, Pastaza, Ecuador
- Universidad Regional Autónoma de los Andes UNIANDES-Puyo, Puyo, Pastaza,Ecuador
| | - Humbert González-Díaz
- Dept.
of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940 Leioa, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| |
Collapse
|
37
|
Mallik MK. An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network. J Theor Biol 2018; 438:78-91. [DOI: 10.1016/j.jtbi.2017.11.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Revised: 10/12/2017] [Accepted: 11/02/2017] [Indexed: 01/22/2023]
|
38
|
Konini S, Janse van Rensburg EJ. Mean field analysis of algorithms for scale-free networks in molecular biology. PLoS One 2017; 12:e0189866. [PMID: 29272285 PMCID: PMC5741260 DOI: 10.1371/journal.pone.0189866] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 12/04/2017] [Indexed: 11/25/2022] Open
Abstract
The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k−γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks).
Collapse
Affiliation(s)
- S. Konini
- Mathematics & Statistics, York University, Toronto, Ontario, M3J 1P3, Canada
| | | |
Collapse
|
39
|
Weishaupt H, Johansson P, Engström C, Nelander S, Silvestrov S, Swartling FJ. Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks. Methodol Comput Appl Probab 2017. [DOI: 10.1007/s11009-017-9554-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
|
40
|
Peng C, Lin Y, Luo H, Gao F. A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes. Front Microbiol 2017; 8:2331. [PMID: 29230204 PMCID: PMC5711816 DOI: 10.3389/fmicb.2017.02331] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 11/13/2017] [Indexed: 12/15/2022] Open
Abstract
Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.
Collapse
Affiliation(s)
- Chong Peng
- Department of Physics, School of Science, Tianjin University, Tianjin, China
| | - Yan Lin
- Department of Physics, School of Science, Tianjin University, Tianjin, China
| | - Hao Luo
- Department of Physics, School of Science, Tianjin University, Tianjin, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin, China
- Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, China
| |
Collapse
|
41
|
Mistry D, Wise RP, Dickerson JA. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network. PLoS One 2017; 12:e0187091. [PMID: 29121073 PMCID: PMC5679606 DOI: 10.1371/journal.pone.0187091] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 10/15/2017] [Indexed: 11/18/2022] Open
Abstract
Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.
Collapse
Affiliation(s)
- Divya Mistry
- Bioinformatics and Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa, United States of America
| | - Roger P. Wise
- Bioinformatics and Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Corn Insects and Crop Genetics Research Unit, USDA-Agricultural Research Service, Ames, Iowa, United States of America
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, United States of America
| | - Julie A. Dickerson
- Bioinformatics and Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa, United States of America
| |
Collapse
|
42
|
Centralities in simplicial complexes. Applications to protein interaction networks. J Theor Biol 2017; 438:46-60. [PMID: 29128505 DOI: 10.1016/j.jtbi.2017.11.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/01/2017] [Accepted: 11/07/2017] [Indexed: 01/01/2023]
Abstract
Complex networks can be used to represent complex systems which originate in the real world. Here we study a transformation of these complex networks into simplicial complexes, where cliques represent the simplices of the complex. We extend the concept of node centrality to that of simplicial centrality and study several mathematical properties of degree, closeness, betweenness, eigenvector, Katz, and subgraph centrality for simplicial complexes. We study the degree distributions of these centralities at the different levels. We also compare and describe the differences between the centralities at the different levels. Using these centralities we study a method for detecting essential proteins in PPI networks of cells and explain the varying abilities of the centrality measures at the different levels in identifying these essential proteins.
Collapse
|
43
|
Schmitt O, Badurek S, Liu W, Wang Y, Rabiller G, Kanoke A, Eipert P, Liu J. Prediction of regional functional impairment following experimental stroke via connectome analysis. Sci Rep 2017; 7:46316. [PMID: 28406178 PMCID: PMC5390322 DOI: 10.1038/srep46316] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 03/14/2017] [Indexed: 01/28/2023] Open
Abstract
Recent advances in functional connectivity suggest that shared neuronal activation patterns define brain networks linking anatomically separate brain regions. We sought to investigate how cortical stroke disrupts multiple brain regions in processing spatial information. We conducted a connectome investigation at the mesoscale-level using the neuroVIISAS-framework, enabling the analysis of directed and weighted connectivity in bilateral hemispheres of cortical and subcortical brain regions. We found that spatial-exploration induced brain activation mapped by Fos, a proxy of neuronal activity, was differentially affected by stroke in a region-specific manner. The extent of hypoactivation following spatial exploration is inversely correlated with the spatial distance between the region of interest and region damaged by stroke, in particular within the parietal association and the primary somatosensory cortex, suggesting that the closer a region is to a stroke lesion, the more it would be affected during functional activation. Connectome modelling with 43 network parameters failed to reliably predict regions of hypoactivation in stroke rats exploring a novel environment, despite a modest correlation found for the centrality and hubness parameters in the home-caged animals. Further investigation in the inhibitory versus excitatory neuronal networks and microcircuit connectivity is warranted to improve the accuracy of predictability in post-stroke functional impairment.
Collapse
Affiliation(s)
- O Schmitt
- Department of Anatomy, University of Rostock, Germany
| | - S Badurek
- Department of Neurological Surgery, UCSF, San Francisco, CA 94158, USA.,Department of Neurological Surgery, SFVAMC, San Francisco, CA 94158, USA
| | - W Liu
- Department of Neurological Surgery, UCSF, San Francisco, CA 94158, USA.,Department of Neurological Surgery, SFVAMC, San Francisco, CA 94158, USA.,Department of Neurological Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, PR China
| | - Y Wang
- Department of Neurological Surgery, UCSF, San Francisco, CA 94158, USA.,Department of Neurological Surgery, SFVAMC, San Francisco, CA 94158, USA.,Department of Neurological Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, PR China
| | - G Rabiller
- Department of Neurological Surgery, UCSF, San Francisco, CA 94158, USA.,Department of Neurological Surgery, SFVAMC, San Francisco, CA 94158, USA
| | - A Kanoke
- Department of Neurological Surgery, UCSF, San Francisco, CA 94158, USA.,Department of Neurological Surgery, SFVAMC, San Francisco, CA 94158, USA
| | - P Eipert
- Department of Anatomy, University of Rostock, Germany
| | - J Liu
- Department of Neurological Surgery, UCSF, San Francisco, CA 94158, USA.,Department of Neurological Surgery, SFVAMC, San Francisco, CA 94158, USA
| |
Collapse
|
44
|
Mao Y, Kuo SW, Chen L, Heckman CJ, Jiang MC. The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis. PLoS One 2017; 12:e0172246. [PMID: 28282387 PMCID: PMC5345759 DOI: 10.1371/journal.pone.0172246] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Accepted: 02/01/2017] [Indexed: 12/12/2022] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative disease characterized by selective loss of motoneurons. While several breakthroughs have been made in identifying ALS genetic defects, the detailed molecular mechanisms are still unclear. These genetic defects involve in numerous biological processes, which converge to a common destiny: motoneuron degeneration. In addition, the common comorbid Frontotemporal Dementia (FTD) further complicates the investigation of ALS etiology. In this study, we aimed to explore the protein-protein interaction network built on known ALS-causative genes to identify essential proteins and common downstream proteins between classical ALS and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that classical ALS and ALS+FTD share similar essential protein set (VCP, FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment profiles. Thus, disruptions to these essential proteins might cause motoneuron susceptible to cellular stresses and eventually vulnerable to proteinopathies. Moreover, we identified a common downstream protein, ubiquitin-C, extensively interconnected with ALS-causative proteins (22 out of 24) which was not linked to ALS previously. Our in silico approach provides the computational background for identifying ALS therapeutic targets, and points out the potential downstream common ground of ALS-causative mutations.
Collapse
Affiliation(s)
- Yimin Mao
- Applied Science Institute, Jiangxi University of Science and Technology, Jiangxi, China
- Department of Physiology, Northwestern University, Chicago, Illinois, United States of America
| | - Su-Wei Kuo
- Department of Physiology, Northwestern University, Chicago, Illinois, United States of America
| | - Le Chen
- Applied Science Institute, Jiangxi University of Science and Technology, Jiangxi, China
| | - C. J. Heckman
- Department of Physiology, Northwestern University, Chicago, Illinois, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, United States of America
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois, United States of America
| | - M. C. Jiang
- Department of Physiology, Northwestern University, Chicago, Illinois, United States of America
- * E-mail:
| |
Collapse
|
45
|
Li M, Lu Y, Niu Z, Wu FX. United Complex Centrality for Identification of Essential Proteins from PPI Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:370-380. [PMID: 28368815 DOI: 10.1109/tcbb.2015.2394487] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Essential proteins are indispensable for the survival or reproduction of an organism. Identification of essential proteins is not only necessary for the understanding of the minimal requirements for cellular life, but also important for the disease study and drug design. With the development of high-throughput techniques, a large number of protein-protein interaction data are available, which promotes the studies of essential proteins from the network level. Up to now, though a series of computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a new method, United complex Centrality (UC), to identify essential proteins by integrating the protein complexes with the topological features of protein-protein interaction (PPI) networks. By analyzing the relationship between the essential proteins and the known protein complexes of S. cerevisiae and human, we find that the proteins in complexes are more likely to be essential compared with the proteins not included in any complexes and the proteins appeared in multiple complexes are more inclined to be essential compared to those only appeared in a single complex. Considering that some protein complexes generated by computational methods are inaccurate, we also provide a modified version of UC with parameter alpha, named UC-P. The experimental results show that protein complex information can help identify the essential proteins more accurate both for the PPI network of S. cerevisiae and that of human. The proposed method UC performs obviously better than the eight previously proposed methods (DC, IC, EC, SC, BC, CC, NC, and LAC) for identifying essential proteins.
Collapse
|
46
|
Folador EL, de Carvalho PVSD, Silva WM, Ferreira RS, Silva A, Gromiha M, Ghosh P, Barh D, Azevedo V, Röttger R. In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks. BMC SYSTEMS BIOLOGY 2016; 10:103. [PMID: 27814699 PMCID: PMC5097352 DOI: 10.1186/s12918-016-0346-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 10/18/2016] [Indexed: 12/27/2022]
Abstract
Background Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Results Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. Conclusions The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0346-4) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Edson Luiz Folador
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.,Institute of Biological Sciences, Federal University of Para, Belém, PA, Brazil.,Biotechnology Center (CBiotec), Federal University of Paraiba (UFPB), João Pessoa, Brazil
| | - Paulo Vinícius Sanches Daltro de Carvalho
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Wanderson Marques Silva
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Rafaela Salgado Ferreira
- Department of Biochemistry and Immunology, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Artur Silva
- Institute of Biological Sciences, Federal University of Para, Belém, PA, Brazil
| | - Michael Gromiha
- Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Tamilnadu, India
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Debmalya Barh
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal, India
| | - Vasco Azevedo
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
| |
Collapse
|
47
|
Li G, Li M, Wang J, Wu J, Wu FX, Pan Y. Predicting essential proteins based on subcellular localization, orthology and PPI networks. BMC Bioinformatics 2016; 17 Suppl 8:279. [PMID: 27586883 PMCID: PMC5009824 DOI: 10.1186/s12859-016-1115-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Background Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods. Results Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION. Conclusions We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.
Collapse
Affiliation(s)
- Gaoshi Li
- School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.,Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, People's Republic of China
| | - Min Li
- School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.
| | - Jingli Wu
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, People's Republic of China
| | - Fang-Xiang Wu
- School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.,Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9, SK, Canada
| | - Yi Pan
- School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.,Department of Computer Science, Georgia State University, Atlanta, 30302-4110, GA, USA
| |
Collapse
|
48
|
Zhang X, Xiao W, Acencio ML, Lemke N, Wang X. An ensemble framework for identifying essential proteins. BMC Bioinformatics 2016; 17:322. [PMID: 27557880 PMCID: PMC4997703 DOI: 10.1186/s12859-016-1166-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 08/09/2016] [Indexed: 11/10/2022] Open
Abstract
Background Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. Results In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. Conclusions This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1166-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Xue Zhang
- Systems Biology Core, NHLBI, NIH, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Wangxin Xiao
- Department of Computer Science, XiangNan University, Eastern Wangxian Park, Chenzhou, Hunan, 423000, China.
| | - Marcio Luis Acencio
- Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP-São Paulo State University, CEP 18618-970, Botucatu, São Paulo, 510, Brazil.,Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), P.B. 8905, N-7491, Trondheim, Norway
| | - Ney Lemke
- Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP-São Paulo State University, CEP 18618-970, Botucatu, São Paulo, 510, Brazil
| | - Xujing Wang
- Systems Biology Core, NHLBI, NIH, 9000 Rockville Pike, Bethesda, MD, 20892, USA.
| |
Collapse
|
49
|
Shang X, Wang Y, Chen B. Identifying essential proteins based on dynamic protein-protein interaction networks and RNA-Seq datasets. SCIENCE CHINA INFORMATION SCIENCES 2016; 59:070106. [DOI: 10.1007/s11432-016-5583-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2024]
|
50
|
Gan Y, Zheng S, Baak JP, Zhao S, Zheng Y, Luo N, Liao W, Fu C. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis. Acta Pharm Sin B 2015; 5:590-5. [PMID: 26713275 PMCID: PMC4675814 DOI: 10.1016/j.apsb.2015.09.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 08/14/2015] [Accepted: 09/11/2015] [Indexed: 12/16/2022] Open
Abstract
Curcumin, the medically active component from Curcuma longa (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein–protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation.
Collapse
Key Words
- Anti-inflammatory
- Curcumin
- Cytoscape
- ETS, erythroblast transformation-specific
- GO, gene ontology
- Gene ontology enrichment analysis
- IFNs, interferons
- IL, interleukin
- JAK-STAT, Janus kinase-STAT
- MAPK, mitogen-activated protein kinase
- MCODE, molecular complex detection
- Module
- Molecular complex detection
- Molecular mechanism
- NF-κB, nuclear factor kappa B
- PIN, protein interaction network
- PPIs, protein–protein interactions
- Protein interaction network
- STATs, signal transducer and activator of transcription complexes
- TLR, toll-like receptor
Collapse
|