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Lanciano T, Savino A, Porcu F, Cittaro D, Bonchi F, Provero P. Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data. Gigascience 2022; 12:giad010. [PMID: 36852877 PMCID: PMC9972522 DOI: 10.1093/gigascience/giad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/30/2022] [Accepted: 02/08/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Biological networks are often used to describe the relationships between relevant entities, particularly genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions or networks obtained with different techniques. FINDINGS We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between 2 networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We demonstrate the use of contrast subgraphs in the comparison of coexpression networks derived from different subtypes of breast cancer, coexpression networks derived from transcriptomic and proteomic data, and protein-protein interaction networks assayed in different cell lines. CONCLUSIONS These examples demonstrate how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between 2 conditions or experimental techniques.
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Affiliation(s)
| | - Aurora Savino
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin 10126, Italy
| | | | - Davide Cittaro
- Center for Omics Sciences, San Raffaele Scientific Institute IRCSS, Milan 20132, Italy
| | | | - Paolo Provero
- Center for Omics Sciences, San Raffaele Scientific Institute IRCSS, Milan 20132, Italy
- Department of Neurosciences “Rita Levi Montalcini,” University of Turin, Turin 10126, Italy
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2
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Sahoo TR, Vipsita S, Patra S. Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques. Interdiscip Sci 2022:10.1007/s12539-022-00541-z. [PMID: 36306022 DOI: 10.1007/s12539-022-00541-z] [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: 07/11/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.
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Affiliation(s)
| | - Swati Vipsita
- CSE, IIIT Bhubaneswar, Gothapatna, Bhubaneswar, Odisha, 751003, India
| | - Sabyasachi Patra
- CSE, IIIT Bhubaneswar, Gothapatna, Bhubaneswar, Odisha, 751003, India
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3
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Xue S, Rogers LR, Zheng M, He J, Piermarocchi C, Mias GI. Applying differential network analysis to longitudinal gene expression in response to perturbations. Front Genet 2022; 13:1026487. [PMID: 36324501 PMCID: PMC9618823 DOI: 10.3389/fgene.2022.1026487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022] Open
Abstract
Differential Network (DN) analysis is a method that has long been used to interpret changes in gene expression data and provide biological insights. The method identifies the rewiring of gene networks in response to external perturbations. Our study applies the DN method to the analysis of RNA-sequencing (RNA-seq) time series datasets. We focus on expression changes: (i) in saliva of a human subject after pneumococcal vaccination (PPSV23) and (ii) in primary B cells treated ex vivo with a monoclonal antibody drug (Rituximab). The DN method enabled us to identify the activation of biological pathways consistent with the mechanisms of action of the PPSV23 vaccine and target pathways of Rituximab. The community detection algorithm on the DN revealed clusters of genes characterized by collective temporal behavior. All saliva and some B cell DN communities showed characteristic time signatures, outlining a chronological order in pathway activation in response to the perturbation. Moreover, we identified early and delayed responses within network modules in the saliva dataset and three temporal patterns in the B cell data.
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Affiliation(s)
- Shuyue Xue
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Lavida R.K. Rogers
- Department of Biological Sciences, University of the Virgin Islands, St Thomas, US Virgin Islands
| | - Minzhang Zheng
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Jin He
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
| | - George I. Mias
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
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4
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Wu Z, Xu J, Nürnberger A, Sabel BA. Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study. Cereb Cortex 2022; 33:4729-4739. [PMID: 36197322 DOI: 10.1093/cercor/bhac375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities ("modules") during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients' recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.
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Affiliation(s)
- Zheng Wu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany.,Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Gebaeude 29, Universitaetsplatz 2, Magdeburg 39106, Germany
| | - Jiahua Xu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany.,Hertie Institute for Clinical Brain Research, Department Neurology and Stroke, Hoppe-Seyler-Strasse 3, Tübingen 72076, Germany
| | - Andreas Nürnberger
- Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Gebaeude 29, Universitaetsplatz 2, Magdeburg 39106, Germany
| | - Bernhard A Sabel
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany
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5
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Dimitrova A, Sferra G, Scippa GS, Trupiano D. Network-Based Analysis to Identify Hub Genes Involved in Spatial Root Response to Mechanical Constrains. Cells 2022; 11:cells11193121. [PMID: 36231084 PMCID: PMC9564363 DOI: 10.3390/cells11193121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Previous studies report that the asymmetric response, observed along the main poplar woody bent root axis, was strongly related to both the type of mechanical forces (compression or tension) and the intensity of force displacement. Despite a large number of targets that have been proposed to trigger this asymmetry, an understanding of the comprehensive and synergistic effect of the antistress spatially related pathways is still lacking. Recent progress in the bioinformatics area has the potential to fill these gaps through the use of in silico studies, able to investigate biological functions and pathway overlaps, and to identify promising targets in plant responses. Presently, for the first time, a comprehensive network-based analysis of proteomic signatures was used to identify functions and pivotal genes involved in the coordinated signalling pathways and molecular activities that asymmetrically modulate the response of different bent poplar root sectors and sides. To accomplish this aim, 66 candidate proteins, differentially represented across the poplar bent root sides and sectors, were grouped according to their abundance profile patterns and mapped, together with their first neighbours, on a high-confidence set of interactions from STRING to compose specific cluster-related subnetworks (I–VI). Successively, all subnetworks were explored by a functional gene set enrichment analysis to identify enriched gene ontology terms. Subnetworks were then analysed to identify the genes that are strongly interconnected with other genes (hub gene) and, thus, those that have a pivotal role in the bent root asymmetric response. The analysis revealed novel information regarding the response coordination, communication, and potential signalling pathways asymmetrically activated along the main root axis, delegated mainly to Ca2+ (for new lateral root formation) and ROS (for gravitropic response and lignin accumulation) signatures. Furthermore, some of the data indicate that the concave side of the bent sector, where the mechanical forces are most intense, communicates to the other (neighbour and distant) sectors, inducing spatially related strategies to ensure water uptake and accompanying cell modification. This information could be critical for understanding how plants maintain and improve their structural integrity—whenever and wherever it is necessary—in natural mechanical stress conditions.
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Verma RK, Kalyakulina A, Mishra A, Ivanchenko M, Jalan S. Role of mitochondrial genetic interactions in determining adaptation to high altitude human population. Sci Rep 2022; 12:2046. [PMID: 35132109 PMCID: PMC8821606 DOI: 10.1038/s41598-022-05719-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 12/17/2021] [Indexed: 12/13/2022] Open
Abstract
Physiological and haplogroup studies performed to understand high-altitude adaptation in humans are limited to individual genes and polymorphic sites. Due to stochastic evolutionary forces, the frequency of a polymorphism is affected by changes in the frequency of a near-by polymorphism on the same DNA sample making them connected in terms of evolution. Here, first, we provide a method to model these mitochondrial polymorphisms as "co-mutation networks" for three high-altitude populations, Tibetan, Ethiopian and Andean. Then, by transforming these co-mutation networks into weighted and undirected gene-gene interaction (GGI) networks, we were able to identify functionally enriched genetic interactions of CYB and CO3 genes in Tibetan and Andean populations, while NADH dehydrogenase genes in the Ethiopian population playing a significant role in high altitude adaptation. These co-mutation based genetic networks provide insights into the role of different set of genes in high-altitude adaptation in human sub-populations.
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Affiliation(s)
- Rahul K Verma
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India
| | - Alena Kalyakulina
- Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Ankit Mishra
- Complex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India
| | - Mikhail Ivanchenko
- Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Laboratory of Systems Medicine of Healthy Aging and Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Sarika Jalan
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India. .,Complex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India.
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Yang Y, Shi Y, Kerfahi D, Ogwu MC, Wang J, Dong K, Takahashi K, Moroenyane I, Adams JM. Elevation-related climate trends dominate fungal co-occurrence network structure and the abundance of keystone taxa on Mt. Norikura, Japan. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 799:149368. [PMID: 34352461 DOI: 10.1016/j.scitotenv.2021.149368] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/15/2021] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
Soil fungi play an important role in promoting nutrient cycling and maintaining ecosystem stability. Yet, there has been little understanding of how fungal co-occurrence networks differ along elevational climate gradients, a topic of interest to both macroecology and climate change studies. Based on high-throughput sequencing technology, we investigated the trend in co-occurrence network structure of soil fungal communities at 11 elevation levels along a 2300 m elevation gradient on Mt. Norikura, Japan, and identified the keystone taxa in the network, hypothesizing a progressive decline in network connectivity with elevation due to decreased plant diversity and enhanced environmental stress caused by changes in climate and soil characteristics. Our results demonstrated that network-level topological features such as network size, average degree, clustering coefficient, and modularity decreased significantly with increasing elevation, indicating that the fungal OTUs at low elevation were more closely associated and the network structure was more compact at low elevations. This conclusion was verified by the negative correlation between positive cohesion, negative cohesion and elevation. Moreover, the negative/positive cohesion ratio reached its peak value in mid-elevations with moderate environmental stress, indicating that the fungal community structure in mid-elevations was more stable than that at other elevations. We also found that the keystone taxa were more abundant at lower elevations. Furthermore, statistical analysis revealed that against a background of uniform geology, climate may play a dominant role in determining the properties and intensity of soil fungal networks, and significantly affect the abundance distribution of keystone taxa. These findings enhance understanding of the pattern and mechanism of the fungal community co-occurrence network along elevation, as well as the responses of microorganisms to climate change on a vertical scale in montane ecosystems. IMPORTANCE: Exploration of the elevational distribution of microbial networks and their driving factors and mechanisms may provide opportunities for predicting potential impacts of environmental changes, on ecosystem functions and biogeographic patterns at a broad scale. Although many studies have explored patterns of fungal community diversity and composition along various environmental gradients, it is unclear how the topological structure of co-occurrence networks shifts along elevational temperature gradients. In this study, we found that the connectivity of the fungal community decreased with increasing elevation and that climate was the dominant factor regulating co-occurrence patterns, apparently acting indirectly through soil characteristics. Our results also suggest that higher elevations on mountains have fewer keystone taxa than low elevations. These patterns may be related to the decrease of plant diversity and the increase of environmental stress along elevation gradients.
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Affiliation(s)
- Ying Yang
- School of Geography and Oceanography, Nanjing University, Nanjing, China
| | - Yu Shi
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Henan, China
| | - Dorsaf Kerfahi
- School of Natural Sciences, Department of Biological Sciences, Keimyung University, Daegu, Republic of Korea
| | - Matthew C Ogwu
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Marche - Floristic Research Center of the Apennines, Gran Sasso and Monti della Laga National Park, San Colombo, Barisciano, L'Aquila, Italy
| | - Jianjun Wang
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Ke Dong
- Life Science Major, Kyonggi University, Suwon, South Korea
| | - Koichi Takahashi
- Department of Biological Sciences, Shinsu University, Matsumoto, Japan
| | - Itumeleng Moroenyane
- Institut National Recherche Scientifique Centre, Institut Armand Frappier Santé Biotechnologie, Quebéc, Canada
| | - Jonathan M Adams
- School of Geography and Oceanography, Nanjing University, Nanjing, China.
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Kunz SN, Helkey D, Zitnik M, Phibbs CS, Rigdon J, Zupancic JAF, Profit J. Quantifying the variation in neonatal transport referral patterns using network analysis. J Perinatol 2021; 41:2795-2803. [PMID: 34035453 PMCID: PMC8613294 DOI: 10.1038/s41372-021-01091-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/31/2021] [Accepted: 04/30/2021] [Indexed: 12/03/2022]
Abstract
OBJECTIVE Regionalized care reduces neonatal morbidity and mortality. This study evaluated the association of patient characteristics with quantitative differences in neonatal transport networks. STUDY DESIGN We retrospectively analyzed prospectively collected data for infants <28 days of age acutely transported within California from 2008 to 2012. We generated graphs representing bidirectional transfers between hospitals, stratified by patient attribute, and compared standard network analysis metrics. RESULT We analyzed 34,708 acute transfers, representing 1594 unique transfer routes between 271 hospitals. Density, centralization, efficiency, and modularity differed significantly among networks drawn based on different infant attributes. Compared to term infants and to those transported for medical reasons, network metrics identify greater degrees of regionalization for preterm and surgical patients (more centralized and less dense, respectively [p < 0.001]). CONCLUSION Neonatal interhospital transport networks differ by patient attributes as reflected by differences in network metrics, suggesting that regionalization should be considered in the context of a multidimensional system.
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Affiliation(s)
- Sarah N. Kunz
- Division of Newborn Medicine, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA,Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Daniel Helkey
- Department of Pediatrics – Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Palo Alto, California, USA,California Perinatal Quality Care Collaborative, Palo Alto, California, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts, USA
| | - Ciaran S. Phibbs
- Department of Pediatrics – Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Palo Alto, California, USA,Health Economics Resource Center, Veterans Affairs Palo Alto Healthcare Systm, Menlo Park, California, USA
| | - Joseph Rigdon
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - John A. F. Zupancic
- Division of Newborn Medicine, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA,Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Jochen Profit
- Department of Pediatrics – Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Palo Alto, California, USA,California Perinatal Quality Care Collaborative, Palo Alto, California, USA
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Agamah FE, Damena D, Skelton M, Ghansah A, Mazandu GK, Chimusa ER. Network-driven analysis of human-Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets. Malar J 2021; 20:421. [PMID: 34702263 PMCID: PMC8547565 DOI: 10.1186/s12936-021-03955-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/16/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The emergence and spread of malaria drug resistance have resulted in the need to understand disease mechanisms and importantly identify essential targets and potential drug candidates. Malaria infection involves the complex interaction between the host and pathogen, thus, functional interactions between human and Plasmodium falciparum is essential to obtain a holistic view of the genetic architecture of malaria. Several functional interaction studies have extended the understanding of malaria disease and integrating such datasets would provide further insights towards understanding drug resistance and/or genetic resistance/susceptibility, disease pathogenesis, and drug discovery. METHODS This study curated and analysed data including pathogen and host selective genes, host and pathogen protein sequence data, protein-protein interaction datasets, and drug data from literature and databases to perform human-host and P. falciparum network-based analysis. An integrative computational framework is presented that was developed and found to be reasonably accurate based on various evaluations, applications, and experimental evidence of outputs produced, from data-driven analysis. RESULTS This approach revealed 8 hub protein targets essential for parasite and human host-directed malaria drug therapy. In a semantic similarity approach, 26 potential repurposable drugs involved in regulating host immune response to inflammatory-driven disorders and/or inhibiting residual malaria infection that can be appropriated for malaria treatment. Further analysis of host-pathogen network shortest paths enabled the prediction of immune-related biological processes and pathways subverted by P. falciparum to increase its within-host survival. CONCLUSIONS Host-pathogen network analysis reveals potential drug targets and biological processes and pathways subverted by P. falciparum to enhance its within malaria host survival. The results presented have implications for drug discovery and will inform experimental studies.
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Affiliation(s)
- Francis E Agamah
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Delesa Damena
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Michelle Skelton
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Anita Ghansah
- College of Health Sciences, Noguchi Memorial Institute for Medical Research, University of Ghana, P.O. Box LG 581, Legon, Ghana
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
- African Institute for Mathematical Sciences, 5-7 Melrose Road, Muizenberg, Cape Town, 7945, South Africa.
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
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Lecca P. Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge. FRONTIERS IN BIOINFORMATICS 2021; 1:746712. [PMID: 36303798 PMCID: PMC9581010 DOI: 10.3389/fbinf.2021.746712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/08/2021] [Indexed: 11/13/2022] Open
Abstract
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.
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11
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Ma H, Liu Z, Zhang X, Zhang L, Jiang H. Balancing topology structure and node attribute in evolutionary multi-objective community detection for attributed networks. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Lu X, Liu F, Miao Q, Liu P, Gao Y, He K. A novel method to identify gene interaction patterns. BMC Genomics 2021; 22:436. [PMID: 34112093 PMCID: PMC8194229 DOI: 10.1186/s12864-021-07628-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 04/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Gene interaction patterns, including modules and motifs, can be used to identify cancer specific biomarkers and to reveal the mechanism of tumorigenesis. Most of the existing module network inferencing methods focus on gene independent functional patterns, while the studies of overlapping characteristics between modules are lacking. The objective of this study was to reveal the functional overlapping patterns in gene modules, helping elucidate the regulatory relationship between overlapping genes and communities, as well as to explore cancer formation and progression. RESULTS We analyzed six cancer datasets from The Cancer Genome Atlas and obtained three kinds of gene functional modules for each cancer, including Independent-Community, Dependent-Community and Merged-Community. In the six cancers, 59(3.5%) Independent-Communities were identified, while 1631(96.5%) Dependent-Communities were acquired. Compared with Lemon-Tree and K-Means, the gene communities identified by our method were enriched in more known GO categories with lower p-values. Meanwhile, those identified distinguishing communities can significantly distinguish the survival prognostic of patients by Kaplan-Meier analysis. Furthermore, identified driver genes in the gene communities can be considered as biomarkers which can accurately distinguish the tumour or normal samples for each cancer type. CONCLUSIONS In all identified communities, Dependent-Communities are the majority. Our method is more effective than the other two methods which do not consider the overlapping characteristics of modules. This indicates that overlapping genes are located in different specific functional groups, and a communication bridge is established between the communities to construct a comprehensive carcinogenesis.
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Affiliation(s)
- Xinguo Lu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Road, Changsha, 410082, China.
| | - Fang Liu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Road, Changsha, 410082, China
| | - Qiumai Miao
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Road, Changsha, 410082, China
| | - Ping Liu
- Hunan Want Want Hospital, Renmin Zhong Road, Changsha, 410006, China
| | - Yan Gao
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Road, Changsha, 410082, China
| | - Keren He
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Road, Changsha, 410082, China
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Sarkar S, Hubbard JB, Halter M, Plant AL. Information Thermodynamics and Reducibility of Large Gene Networks. ENTROPY 2021; 23:e23010063. [PMID: 33401415 PMCID: PMC7824329 DOI: 10.3390/e23010063] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/24/2020] [Accepted: 12/28/2020] [Indexed: 02/04/2023]
Abstract
Gene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the state of GRNs. Here, we explore how the topology of the interactions between network components may indicate whether the effective state of a GRN can be represented by a small subset of genes. We use methods from information theory to model the regulatory interactions in GRNs as cascading and superposing information channels. We propose an information loss function that enables identification of the conditions by which a small set of genes can represent the state of all the other genes in the network. This information-theoretic analysis extends to a measure of free energy change due to communication within the network, which provides a new perspective on the reducibility of GRNs. Both the information loss and relative free energy depend on the density of interactions and edge communication error in a network. Therefore, this work indicates that a loss in mutual information between genes in a GRN is directly coupled to a thermodynamic cost, i.e., a reduction of relative free energy, of the system.
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14
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Savino A, Provero P, Poli V. Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression. Int J Mol Sci 2020; 21:ijms21249461. [PMID: 33322692 PMCID: PMC7764314 DOI: 10.3390/ijms21249461] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/02/2020] [Accepted: 12/09/2020] [Indexed: 02/02/2023] Open
Abstract
Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.
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Affiliation(s)
- Aurora Savino
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
- Correspondence: (A.S.); (V.P.)
| | - Paolo Provero
- Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Corso Massimo D’Ázeglio 52, 10126 Turin, Italy;
- Center for Omics Sciences, Ospedale San Raffaele IRCCS, Via Olgettina 60, 20132 Milan, Italy
| | - Valeria Poli
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
- Correspondence: (A.S.); (V.P.)
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15
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Patra S, Mohapatra A. Protein complex prediction in interaction network based on network motif. Comput Biol Chem 2020; 89:107399. [PMID: 33152665 DOI: 10.1016/j.compbiolchem.2020.107399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 08/07/2020] [Accepted: 10/01/2020] [Indexed: 11/28/2022]
Abstract
The enormous size of Protein-Protein Interaction (PPI) networks demands efficient computational methods to extract biologically significant protein complexes. A wide variety of algorithms have been proposed to predict protein complexes from PPI networks. However, it is still a challenging task to detect protein complexes with high accuracy and manageable sensitivity. In this manuscript, a novel complex prediction algorithm based on Network Motif (CPNM) is proposed. This algorithm addresses the role of proteins in the embeddings of network motif. These roles are used to define feature vectors and feature weights of proteins. Based on these features, a neighborhood search technique predict the protein complexes that consider both the inherent organization of proteins as well as the dense regions in PPI networks. The performance of the proposed algorithm is evaluated using various evaluation metrics like Precision, Recall, F-measure, Sensitivity, PPV, and Accuracy. The research finding indicates that the proposed algorithm outperforms most of the competing algorithms like MCODE, DPClus, RNSC, COACH, ClusterONE, CMC and PROCODE over the PPI network of Saccharomyces cerevisiae and Homo sapiens.
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Affiliation(s)
- Sabyasachi Patra
- Bioinformatics Lab, Department of Computer Science, IIIT, Bhubaneswar, India.
| | - Anjali Mohapatra
- Bioinformatics Lab, Department of Computer Science, IIIT, Bhubaneswar, India.
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16
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Yang Y, Liu H, Guan Z, He X, Liu G. CoHomo: A cluster-attribute correlation aware graph clustering framework. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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17
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Romero-Molina S, Ruiz-Blanco YB, Green JR, Sanchez-Garcia E. ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins. Protein Sci 2020; 28:1734-1743. [PMID: 31271472 DOI: 10.1002/pro.3673] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 06/21/2019] [Accepted: 06/24/2019] [Indexed: 12/24/2022]
Abstract
Computational tools for the analysis of protein data and the prediction of biological properties are essential in life sciences and biomedical research. Here, we introduce ProtDCal-Suite, a web server comprising a set of machine learning-based methods for studying proteins. The main module of ProtDCal-Suite is the ProtDCal software. ProtDCal translates the structural information of proteins into numerical descriptors that serve as input to machine-learning techniques. The ProtDCal-Suite server also incorporates a post-processing optional stage that allows ranking and filtering the obtained descriptors by computing their Shannon entropy values across the input set of proteins. ProtDCal's codification was used in the development of models for the prediction of specific protein properties. Thus, the other modules of ProtDCal-Suite are protein analysis tools implemented using ProtDCal's descriptors. Among them are PPI-Detect, for predicting the interaction likelihood of protein-protein and protein-peptide pairs, Enzyme Identifier, for identifying enzymes from amino acid sequences or 3D structures, and Pred-NGlyco, for predicting N-glycosylation sites. ProtDCal-Suite is freely accessible at https://protdcal.zmb.uni-due.de.
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Affiliation(s)
- Sandra Romero-Molina
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Yasser B Ruiz-Blanco
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - James R Green
- Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Elsa Sanchez-Garcia
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany
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18
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Singh V, Singh G, Singh V. TulsiPIN: An Interologous Protein Interactome of Ocimum tenuiflorum. J Proteome Res 2020; 19:884-899. [PMID: 31789043 DOI: 10.1021/acs.jproteome.9b00683] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Ocimum tenuiflorum, commonly known as holy basil or tulsi, is globally recognized for its multitude of medicinal properties. However, a comprehensive study revealing the complex interplay among its constituent proteins at subcellular level is still lacking. To bridge this gap, in this work, a genome-scale interologous protein-protein interaction (PPI) network, TulsiPIN, is developed using 36 template plants, which consists of 13 660 nodes and 327 409 binary interactions. A high confidence network, hc-TulsiPIN, consisting of 7719 nodes having 95 532 interactions is inferred using domain-domain interaction information along with interolog-based statistics, and its reliability is assessed using pathway enrichment, functional homogeneity, and protein colocalization of PPIs. Examination of topological features revealed that hc-TulsiPIN possesses conventional properties, like small-world, scale-free, and modular architecture. A total of 1625 vital proteins are predicted by statistically evaluating hc-TulsiPIN with two ensembles of corresponding random networks, each consisting of 10 000 realizations of Erdoős-Rényi and Barabási-Albert models. Also, numerous regulatory proteins like transcription factors, transcription regulators, and protein kinases are profiled. Using 36 guide genes participating in 9 secondary metabolite biosynthetic pathways, a subnetwork consisting of 171 proteins and 612 interactions was constructed, and 127 of these proteins could be successfully characterized. Detailed information of TulsiPIN is available at https://cuhpcbbtulsipin.shinyapps.io/tulsipin_v0/ .
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Affiliation(s)
- Vikram Singh
- Centre for Computational Biology and Bioinformatics , Central University of Himahcal Pradesh , Dharamshala 176206 , India
| | - Gagandeep Singh
- Centre for Computational Biology and Bioinformatics , Central University of Himahcal Pradesh , Dharamshala 176206 , India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics , Central University of Himahcal Pradesh , Dharamshala 176206 , India
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19
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Wang R, Liu G, Wang C. Identifying protein complexes based on an edge weight algorithm and core-attachment structure. BMC Bioinformatics 2019; 20:471. [PMID: 31521132 PMCID: PMC6744658 DOI: 10.1186/s12859-019-3007-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 07/26/2019] [Indexed: 02/02/2023] Open
Abstract
Background Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins. Results In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy. Conclusions In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA.
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Affiliation(s)
- Rongquan Wang
- College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China
| | - Guixia Liu
- College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China. .,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China.
| | - Caixia Wang
- School of International Economics, China Foreign Affairs University, 24 Zhanlanguan Road, Xicheng District, Beijing, 100037, China
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20
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Lei H, Liu W, Si J, Wang J, Zhang T. Analyzing the regulation of miRNAs on protein-protein interaction network in Hodgkin lymphoma. BMC Bioinformatics 2019; 20:449. [PMID: 31477006 PMCID: PMC6720096 DOI: 10.1186/s12859-019-3041-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 08/21/2019] [Indexed: 12/28/2022] Open
Abstract
Background Hodgkin Lymphoma (HL) is a type of aggressive malignancy in lymphoma that has high incidence in young adults and elderly patients. Identification of reliable diagnostic markers and efficient therapeutic targets are especially important for the diagnosis and treatment of HL. Although many HL-related molecules have been identified, our understanding on the molecular mechanisms underlying the disease is still far from complete due to its complex and heterogeneous characteristics. In such situation, exploring the molecular mechanisms underlying HL via systems biology approaches provides a promising option. In this study, we try to elucidate the molecular mechanisms related to the disease and identify potential pharmaceutical targets from a network-based perspective. Results We constructed a series of network models. Based on the analysis of these networks, we attempted to identify the biomarkers and elucidate the molecular mechanisms underlying HL. Initially, we built three different but related protein networks, i.e., background network, HL-basic network and HL-specific network. By analyzing these three networks, we investigated the connection characteristic of the HL-related proteins. Subsequently, we explored the miRNA regulation on HL-specific network and analyzed three kinds of simple regulation patterns, i.e., co-regulation of protein pairs, as well as the direct and indirect regulation of triple proteins. Finally, we constructed a simplified protein network combined with the regulation of miRNAs on proteins to better understand the relation between HL-related proteins and miRNAs. Conclusions We find that the HL-related proteins are more likely to connect with each other compared to other proteins. Moreover, the HL-specific network can be further divided into five sub-networks and 49 proteins as the backbone of HL-specific network make up and connect these 5 sub-networks. Thus, they may be closely associated with HL. In addition, we find that the co-regulation of protein pairs is the main regulatory pattern of miRNAs on the protein network in the HL-specific network. According to the regulation of miRNA on protein network, we have identified 5 core miRNAs as the potential biomarkers for diagnostic of HL. Finally, several protein pathways have been identified to closely associated with HL, which provides deep insights into underlying mechanism of HL. Electronic supplementary material The online version of this article (10.1186/s12859-019-3041-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Huimin Lei
- School of Biomedical Engineering, Tianjin Medical University, Tianjin, China.,School of Continuation Education, Tianjin Medical University, Tianjin, China
| | - Wenxu Liu
- School of Biomedical Engineering, Tianjin Medical University, Tianjin, China
| | - Jiarui Si
- School of Basic Medicine, Tianjin Medical University, Tianjin, China
| | - Ju Wang
- School of Biomedical Engineering, Tianjin Medical University, Tianjin, China
| | - Tao Zhang
- School of Biomedical Engineering, Tianjin Medical University, Tianjin, China.
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21
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Wan C, Cozzetto D, Fa R, Jones DT. Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks. PLoS One 2019; 14:e0209958. [PMID: 31335894 PMCID: PMC6650051 DOI: 10.1371/journal.pone.0209958] [Citation(s) in RCA: 7] [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: 12/13/2018] [Accepted: 07/01/2019] [Indexed: 12/02/2022] Open
Abstract
Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other protein-protein interaction network-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.
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Affiliation(s)
- Cen Wan
- Bioinformatics Group, Department of Computer Science, University College London, London, United Kingdom
- Biomedical Data Science Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Domenico Cozzetto
- Bioinformatics Group, Department of Computer Science, University College London, London, United Kingdom
- Biomedical Data Science Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Rui Fa
- Bioinformatics Group, Department of Computer Science, University College London, London, United Kingdom
- Biomedical Data Science Laboratory, The Francis Crick Institute, London, United Kingdom
| | - David T. Jones
- Bioinformatics Group, Department of Computer Science, University College London, London, United Kingdom
- Biomedical Data Science Laboratory, The Francis Crick Institute, London, United Kingdom
- * E-mail:
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22
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Romero-Molina S, Ruiz-Blanco YB, Harms M, Münch J, Sanchez-Garcia E. PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions. J Comput Chem 2019; 40:1233-1242. [PMID: 30768790 DOI: 10.1002/jcc.25780] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 11/29/2018] [Accepted: 12/29/2018] [Indexed: 12/18/2022]
Abstract
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets. However, the numerical codification of these datasets often influences the performance of data mining approaches. Here, we introduce a procedure for the general-purpose numerical codification of polypeptides. This procedure transforms pairs of amino acid sequences into a machine learning-friendly vector, whose elements represent numerical descriptors of residues in proteins. We used this numerical encoding procedure for the development of a support vector machine model (PPI-Detect), which allows predicting whether two proteins will interact or not. PPI-Detect (https://ppi-detect.zmb.uni-due.de/) outperforms state of the art sequence-based predictors of PPI. We employed PPI-Detect for the analysis of derivatives of EPI-X4, an endogenous peptide inhibitor of CXCR4, a G-protein-coupled receptor. There, we identified with high accuracy those peptides which bind better than EPI-X4 to the receptor. Also using PPI-Detect, we designed a novel peptide and then experimentally established its anti-CXCR4 activity. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Sandra Romero-Molina
- Center of Medical Biotechnology, University of Duisburg-Essen, Duisburg, Germany
| | - Yasser B Ruiz-Blanco
- Center of Medical Biotechnology, University of Duisburg-Essen, Duisburg, Germany
| | - Mirja Harms
- Institute of Molecular Virology, Ulm University Medical Center, Ulm, Germany
| | - Jan Münch
- Institute of Molecular Virology, Ulm University Medical Center, Ulm, Germany.,Core Facility Functional Peptidomics, Ulm University Medical Center, Ulm, Germany
| | - Elsa Sanchez-Garcia
- Center of Medical Biotechnology, University of Duisburg-Essen, Duisburg, Germany
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23
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Tahir RA, Wu H, Javed N, Khalique A, Khan SAF, Mir A, Ahmed MS, Barreto GE, Qing H, Ashraf GM, Sehgal SA. Pharmacoinformatics and molecular docking reveal potential drug candidates against Schizophrenia to target TAAR6. J Cell Physiol 2018; 234:13263-13276. [PMID: 30569503 DOI: 10.1002/jcp.27999] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 11/30/2018] [Indexed: 11/10/2022]
Abstract
Schizophrenia (SZ) is a complex disabling disorder that leads to the mental disability and afflicts 1% of the world's total population and placed in top ten medical disorders. In current work, bioinformatics analyses were carried out on Trace amine (TA)-associated receptor 6 (TAAR6) to recognize the potential drugs and compounds against SZ. Comparative modeling and threading-based approaches were utilized for the structure prediction of TAAR6. Fifty-nine predicted structures were evaluated by various model assessment techniques and final model having only eight amino acids in the outlier region and 98.5% overall quality factor was chosen for further pharmacoinformatics and molecular docking analyses. From an extensive literature review, 11 Food and Drug Administration (FDA) approved drugs were analyzed by computational techniques and Aripiprazole was found as the most effective drug against SZ by targeting TAAR6. Here, we report five novel molecules which exhibited the highest binding affinity, effective drug properties, and interestingly, observed better results than the approved selected drugs against SZ by targeting TAAR6. The docking analyses revealed that Arg-92, Trp-98, Gln-191, Thr-192, Ala-290, Cys-291, Tyr-293, and Glu-294 residues were observed as critical interacting residues in receptor-ligand interactions. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, Lipinski rule of five, highest binding affinity coupled with virtual screening (VS), and pharmacophore modeling approach illustrated that aripiprazole (-8.6 kcal/mol) and TAAR6_0094 (-9.3 kcal/mol) are potential inhibitors for targeting TAAR6. It is suggested that schizophrenic patients have to use Aripiprazole for the medication of SZ by targeting TAAR6 and develop effective therapies by utilizing scrutinized novel compound.
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Affiliation(s)
- Rana Adnan Tahir
- Key Laboratory of Molecular Medicine and Biotherapy in the Ministry of Industry and Information Technology, Department of Biology, School of Life Sciences, Beijing Institute of Technology, Beijing, China.,Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Islamabad, Pakistan
| | - Hao Wu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Naima Javed
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Islamabad, Pakistan
| | - Anila Khalique
- State Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Bioactive Materials of Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China
| | | | - Asif Mir
- Department of Bioinformatics and Biotechnology, International Islamic University Islamabad, Islamabad, Pakistan
| | - Muhammad Saad Ahmed
- Department of Biological Engineering/Institute of Biotransformation and Synthetic Biosystem, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - George E Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hong Qing
- Key Laboratory of Molecular Medicine and Biotherapy in the Ministry of Industry and Information Technology, Department of Biology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Ghulam Md Ashraf
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sheikh Arslan Sehgal
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Islamabad, Pakistan.,University of Chinese Academy of Sciences, Beijing, China
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24
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Ren Y, Ay A, Kahveci T. Shortest path counting in probabilistic biological networks. BMC Bioinformatics 2018; 19:465. [PMID: 30514202 PMCID: PMC6278053 DOI: 10.1186/s12859-018-2480-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Accepted: 11/09/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Biological regulatory networks, representing the interactions between genes and their products, control almost every biological activity in the cell. Shortest path search is critical to apprehend the structure of these networks, and to detect their key components. Counting the number of shortest paths between pairs of genes in biological networks is a polynomial time problem. The fact that biological interactions are uncertain events however drastically complicates the problem, as it makes the topology of a given network uncertain. RESULTS In this paper, we develop a novel method to count the number of shortest paths between two nodes in probabilistic networks. Unlike earlier approaches, which uses the shortest path counting methods that are specifically designed for deterministic networks, our method builds a new mathematical model to express and compute the number of shortest paths. We prove the correctness of this model. CONCLUSIONS We compare our novel method to three existing shortest path counting methods on synthetic and real gene regulatory networks. Our experiments demonstrate that our method is scalable, and it outperforms the existing methods in accuracy. Application of our shortest path counting method to detect communities in probabilistic networks shows that our method successfully finds communities in probabilistic networks. Moreover, our experiments on cell cycle pathway among different cancer types exhibit that our method helps in uncovering key functional characteristics of biological networks.
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Affiliation(s)
- Yuanfang Ren
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA
| | - Ahmet Ay
- Departments of Biology and Mathematics, Colgate University, Hamilton, 13346, NY, USA
| | - Tamer Kahveci
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA.
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25
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Jiao P, Yu W, Wang W, Li X, Sun Y. Exploring temporal community structure and constant evolutionary pattern hiding in dynamic networks. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.065] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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26
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Exploring the Hierarchical Structure of China’s Railway Network from 2008 to 2017. SUSTAINABILITY 2018. [DOI: 10.3390/su10093173] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The analysis of transport networks is an important component of urban and regional development and planning. Based on the four main stages of China’s railway development from 2008 to 2017, this paper analyzes the hierarchical and spatial heterogeneity distribution of train flows. We found a high degree of spatial matching with the distribution of China’s main railway corridors. Then, using a classical community detection algorithm, this paper attempts to describe the functional structure and regional effects of China’s railway network. We also explore the impacts of construction policies and changes to train operations on the spatial organizing pattern and evolution of network hierarchies. The results of this empirical study reveal a clear pattern of independent communities, which in turn indicates the existence of a hierarchical structure in China’s railway network. The decreases in both the number of communities and average distance between community centers indicate that the newer high-speed rail services have shortened the connections between cities. In addition, the detected communities are inconsistent with China’s actual administrative divisions in terms of quantity and boundaries. The spatial spillover and segmentation effects cause the railway network in different regions to be self-contained. Finally, the detected communities in each stage can be divided into the categories of monocentric structure, dual-nuclei structure, and polycentric structure according to the number of extracted hubs. The polycentric structure is the dominant mode, which shows that the railway network has significant spatial dependence and a diversified spatial organization mode. This study has great significance for policymakers seeking to guide the future construction of high-speed rail lines and optimize national or regional railway networks.
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27
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Wang R, Liu G, Wang C, Su L, Sun L. Predicting overlapping protein complexes based on core-attachment and a local modularity structure. BMC Bioinformatics 2018; 19:305. [PMID: 30134824 PMCID: PMC6106838 DOI: 10.1186/s12859-018-2309-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 07/30/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In recent decades, detecting protein complexes (PCs) from protein-protein interaction networks (PPINs) has been an active area of research. There are a large number of excellent graph clustering methods that work very well for identifying PCs. However, most of existing methods usually overlook the inherent core-attachment organization of PCs. Therefore, these methods have three major limitations we should concern. Firstly, many methods have ignored the importance of selecting seed, especially without considering the impact of overlapping nodes as seed nodes. Thus, there may be false predictions. Secondly, PCs are generally supposed to be dense subgraphs. However, the subgraphs with high local modularity structure usually correspond to PCs. Thirdly, a number of available methods lack handling noise mechanism, and miss some peripheral proteins. In summary, all these challenging issues are very important for predicting more biological overlapping PCs. RESULTS In this paper, to overcome these weaknesses, we propose a clustering method by core-attachment and local modularity structure, named CALM, to detect overlapping PCs from weighted PPINs with noises. Firstly, we identify overlapping nodes and seed nodes. Secondly, for a node, we calculate the support function between a node and a cluster. In CALM, a cluster which initially consists of only a seed node, is extended by adding its direct neighboring nodes recursively according to the support function, until this cluster forms a locally optimal modularity subgraph. Thirdly, we repeat this process for the remaining seed nodes. Finally, merging and removing procedures are carried out to obtain final predicted clusters. The experimental results show that CALM outperforms other classical methods, and achieves ideal overall performance. Furthermore, CALM can match more complexes with a higher accuracy and provide a better one-to-one mapping with reference complexes in all test datasets. Additionally, CALM is robust against the high rate of noise PPIN. CONCLUSIONS By considering core-attachment and local modularity structure, CALM could detect PCs much more effectively than some representative methods. In short, CALM could potentially identify previous undiscovered overlapping PCs with various density and high modularity.
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Affiliation(s)
- Rongquan Wang
- College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
| | - Guixia Liu
- College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
| | - Caixia Wang
- School of International Economics, China Foreign Affairs University, 24 Zhanlanguan Road, Xicheng District, Beijing, 100037 China
| | - Lingtao Su
- College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
| | - Liyan Sun
- College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
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28
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Morrison G, Dudte LH, Mahadevan L. Generalized Erdős numbers for network analysis. ROYAL SOCIETY OPEN SCIENCE 2018; 5:172281. [PMID: 30224995 PMCID: PMC6124095 DOI: 10.1098/rsos.172281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 07/09/2018] [Indexed: 06/08/2023]
Abstract
The identification of relationships in complex networks is critical in a variety of scientific contexts. This includes the identification of globally central nodes and analysing the importance of pairwise relationships between nodes. In this paper, we consider the concept of topological proximity (or 'closeness') between nodes in a weighted network using the generalized Erdős numbers (GENs). This measure satisfies a number of desirable properties for networks with nodes that share a finite resource. These include: (i) real-valuedness, (ii) non-locality and (iii) asymmetry. We show that they can be used to define a personalized measure of the importance of nodes in a network with a natural interpretation that leads to new methods to measure centrality. We show that the square of the leading eigenvector of an importance matrix defined using the GENs is strongly correlated with well-known measures such as PageRank, and define a personalized measure of centrality that is also well correlated with other existing measures. The utility of this measure of topological proximity is demonstrated by showing the asymmetries in both the dynamics of random walks and the mean infection time in epidemic spreading are better predicted by the topological definition of closeness provided by the GENs than they are by other measures.
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Affiliation(s)
- Greg Morrison
- Department of Physics, University of Houston, Houston, TX 77204, USA
| | - Levi H. Dudte
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - L. Mahadevan
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
- Kavli Institute for Nano-bio Science and Technology, Harvard University, Cambridge, MA 02138, USA
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29
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A new approach of gene co-expression network inference reveals significant biological processes involved in porcine muscle development in late gestation. Sci Rep 2018; 8:10150. [PMID: 29977047 PMCID: PMC6033925 DOI: 10.1038/s41598-018-28173-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 06/14/2018] [Indexed: 12/28/2022] Open
Abstract
The integration of genetic information in the cellular and nuclear environments is crucial for deciphering the way in which the genome functions under different physiological conditions. Experimental techniques of 3D nuclear mapping, a high-flow approach such as transcriptomic data analyses, and statistical methods for the development of co-expressed gene networks, can be combined to develop an integrated approach for depicting the regulation of gene expression. Our work focused more specifically on the mechanisms involved in the transcriptional regulation of genes expressed in muscle during late foetal development in pig. The data generated by a transcriptomic analysis carried out on muscle of foetuses from two extreme genetic lines for birth mortality are used to construct networks of differentially expressed and co-regulated genes. We developed an innovative co-expression networking approach coupling, by means of an iterative process, a new statistical method for graph inference with data of gene spatial co-localization (3D DNA FISH) to construct a robust network grouping co-expressed genes. This enabled us to highlight relevant biological processes related to foetal muscle maturity and to discover unexpected gene associations between IGF2, MYH3 and DLK1/MEG3 in the nuclear space, genes that are up-regulated at this stage of muscle development.
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30
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Gao X, Liu J, Gong P, Wang J, Fang W, Yan H, Zhu L, Zhou X. Identifying new susceptibility genes on dopaminergic and serotonergic pathways for the framing effect in decision-making. Soc Cogn Affect Neurosci 2018; 12:1534-1544. [PMID: 28431168 PMCID: PMC5629826 DOI: 10.1093/scan/nsx062] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 04/17/2017] [Indexed: 01/03/2023] Open
Abstract
The framing effect refers the tendency to be risk-averse when options are presented positively but be risk-seeking when the same options are presented negatively during decision-making. This effect has been found to be modulated by the serotonin transporter gene (SLC6A4) and the catechol-o-methyltransferase gene (COMT) polymorphisms, which are on the dopaminergic and serotonergic pathways and which are associated with affective processing. The current study aimed to identify new genetic variations of genes on dopaminergic and serotonergic pathways that may contribute to individual differences in the susceptibility to framing. Using genome-wide association data and the gene-based principal components regression method, we examined genetic variations of 26 genes on the pathways in 1317 Chinese Han participants. Consistent with previous studies, we found that the genetic variations of the SLC6A4 gene and the COMT gene were associated with the framing effect. More importantly, we demonstrated that the genetic variations of the aromatic-L-amino-acid decarboxylase (DDC) gene, which is involved in the synthesis of both dopamine and serotonin, contributed to individual differences in the susceptibility to framing. Our findings shed light on the understanding of the genetic basis of affective decision-making.
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Affiliation(s)
- Xiaoxue Gao
- Center for Brain and Cognitive Sciences.,School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China
| | - Jinting Liu
- China Center for Special Economic Zone Research.,Research Centre for Brain Function and Psychological Science, Shenzhen University, Guangdong 518060, China
| | - Pingyuan Gong
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), Northwest University, Shaanxi 710069, China
| | - Junhui Wang
- Research Institute of Educational Technology, South China Normal University, Guangdong 510631, China
| | - Wan Fang
- Peking-Tsinghua Center for Life Sciences.,School of Life Sciences
| | - Hongming Yan
- Peking-Tsinghua Center for Life Sciences.,School of Life Sciences
| | - Lusha Zhu
- Center for Brain and Cognitive Sciences.,Peking-Tsinghua Center for Life Sciences.,PKU-IDG/McGovern Institute for Brain Research
| | - Xiaolin Zhou
- Center for Brain and Cognitive Sciences.,School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China.,PKU-IDG/McGovern Institute for Brain Research.,Key Laboratory of Machine Perception (Ministry of Education).,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China
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31
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Talavera D, Kershaw CJ, Costello JL, Castelli LM, Rowe W, Sims PFG, Ashe MP, Grant CM, Pavitt GD, Hubbard SJ. Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions. Sci Rep 2018; 8:7949. [PMID: 29785040 PMCID: PMC5962585 DOI: 10.1038/s41598-018-26170-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 05/01/2018] [Indexed: 01/30/2023] Open
Abstract
The transcriptional responses of yeast cells to diverse stresses typically include gene activation and repression. Specific stress defense, citric acid cycle and oxidative phosphorylation genes are activated, whereas protein synthesis genes are coordinately repressed. This view was achieved from comparative transcriptomic experiments delineating sets of genes whose expression greatly changed with specific stresses. Less attention has been paid to the biological significance of 1) consistent, albeit modest, changes in RNA levels across multiple conditions, and 2) the global gene expression correlations observed when comparing numerous genome-wide studies. To address this, we performed a meta-analysis of 1379 microarray-based experiments in yeast, and identified 1388 blocks of RNAs whose expression changes correlate across multiple and diverse conditions. Many of these blocks represent sets of functionally-related RNAs that act in a coordinated fashion under normal and stress conditions, and map to global cell defense and growth responses. Subsequently, we used the blocks to analyze novel RNA-seq experiments, demonstrating their utility and confirming the conclusions drawn from the meta-analysis. Our results provide a new framework for understanding the biological significance of changes in gene expression: 'archetypal' transcriptional blocks that are regulated in a concerted fashion in response to external stimuli.
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Affiliation(s)
- David Talavera
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
| | - Christopher J Kershaw
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Joseph L Costello
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.,Department of Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Lydia M Castelli
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.,Sheffield Institute for Translational Neuroscience, The University of Sheffield, Sheffield, United Kingdom
| | - William Rowe
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.,Department of Chemistry, Loughborough University, Loughborough, United Kingdom
| | - Paul F G Sims
- Manchester Institute of Biotechnology (MIB), The University of Manchester, Manchester, United Kingdom
| | - Mark P Ashe
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Chris M Grant
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Graham D Pavitt
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
| | - Simon J Hubbard
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
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32
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Cannoodt R, Ruyssinck J, Ramon J, De Preter K, Saeys Y. IncGraph: Incremental graphlet counting for topology optimisation. PLoS One 2018; 13:e0195997. [PMID: 29698494 PMCID: PMC5919487 DOI: 10.1371/journal.pone.0195997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 04/04/2018] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Graphlets are small network patterns that can be counted in order to characterise the structure of a network (topology). As part of a topology optimisation process, one could use graphlet counts to iteratively modify a network and keep track of the graphlet counts, in order to achieve certain topological properties. Up until now, however, graphlets were not suited as a metric for performing topology optimisation; when millions of minor changes are made to the network structure it becomes computationally intractable to recalculate all the graphlet counts for each of the edge modifications. RESULTS IncGraph is a method for calculating the differences in graphlet counts with respect to the network in its previous state, which is much more efficient than calculating the graphlet occurrences from scratch at every edge modification made. In comparison to static counting approaches, our findings show IncGraph reduces the execution time by several orders of magnitude. The usefulness of this approach was demonstrated by developing a graphlet-based metric to optimise gene regulatory networks. IncGraph is able to quickly quantify the topological impact of small changes to a network, which opens novel research opportunities to study changes in topologies in evolving or online networks, or develop graphlet-based criteria for topology optimisation. AVAILABILITY IncGraph is freely available as an open-source R package on CRAN (incgraph). The development version is also available on GitHub (rcannood/incgraph).
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Affiliation(s)
- Robrecht Cannoodt
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Joeri Ruyssinck
- IDLab, Department of Information Technology, Ghent University – imec, Ghent, Belgium
| | - Jan Ramon
- Department of Computer Science, KU Leuven, Belgium
| | - Katleen De Preter
- Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium
- * E-mail:
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33
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Zhang Z, Song J, Tang J, Xu X, Guo F. Detecting complexes from edge-weighted PPI networks via genes expression analysis. BMC SYSTEMS BIOLOGY 2018; 12:40. [PMID: 29745859 PMCID: PMC5998908 DOI: 10.1186/s12918-018-0565-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
BACKGROUND Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization. RESULTS We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks. CONCLUSIONS On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.
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Affiliation(s)
- Zehua Zhang
- School of Computer Science and Technology, Tianjin University, Tianjin, People’s Republic of China
- Tianjin University Institute of Computational Biology, Tianjin, People’s Republic of China
| | - Jian Song
- School of Computer Science and Technology, Tianjin University, Tianjin, People’s Republic of China
- Tianjin University Institute of Computational Biology, Tianjin, People’s Republic of China
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, People’s Republic of China
| | - Jijun Tang
- School of Computer Science and Technology, Tianjin University, Tianjin, People’s Republic of China
- Tianjin University Institute of Computational Biology, Tianjin, People’s Republic of China
- Department of Computer Science and Engineering, University of South Carolina, Columbia, USA
| | - Xinying Xu
- School of Information Engineering, Taiyuan University of Technology, Taiyuan, People’s Republic of China
| | - Fei Guo
- School of Computer Science and Technology, Tianjin University, Tianjin, People’s Republic of China
- Tianjin University Institute of Computational Biology, Tianjin, People’s Republic of China
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34
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Community Detection in Complex Networks via Clique Conductance. Sci Rep 2018; 8:5982. [PMID: 29654276 PMCID: PMC5899156 DOI: 10.1038/s41598-018-23932-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 03/20/2018] [Indexed: 11/08/2022] Open
Abstract
Network science plays a central role in understanding and modeling complex systems in many areas including physics, sociology, biology, computer science, economics, politics, and neuroscience. One of the most important features of networks is community structure, i.e., clustering of nodes that are locally densely interconnected. Communities reveal the hierarchical organization of nodes, and detecting communities is of great importance in the study of complex systems. Most existing community-detection methods consider low-order connection patterns at the level of individual links. But high-order connection patterns, at the level of small subnetworks, are generally not considered. In this paper, we develop a novel community-detection method based on cliques, i.e., local complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure, the proposed method detects the structure with high fidelity and sensitivity. When applied to networks with no a priori information regarding community structure, the proposed method yields insightful results revealing the organization of these complex networks. We also show that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.
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35
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Abstract
The knowledge of protein-protein interactions (PPIs) and PPI networks (PPINs) is the key to starting to understand the biological processes inside the cell. Many computational tools have been designed to help explore PPIs and PPINs, such as those for interaction detection, reliability assessment and interaction network construction. Here, the application of computational tools is reviewed from three perspectives: PPI database construction, PPI prediction, and interaction network construction and analysis. This overview will provide researchers guidance on choosing appropriate methods for exploring PPIs.
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Affiliation(s)
- Shaowei Dong
- Department of Cell and System Biology, Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada
| | - Nicholas J Provart
- Department of Cell and System Biology, Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.
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36
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Crosara KTB, Moffa EB, Xiao Y, Siqueira WL. Merging in - silico and in vitro salivary protein complex partners using the STRING database: A tutorial. J Proteomics 2018; 171:87-94. [DOI: 10.1016/j.jprot.2017.08.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 07/28/2017] [Accepted: 08/01/2017] [Indexed: 12/20/2022]
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37
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Metri R, Mohan A, Nsengimana J, Pozniak J, Molina-Paris C, Newton-Bishop J, Bishop D, Chandra N. Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach. Sci Rep 2017; 7:17314. [PMID: 29229936 PMCID: PMC5725601 DOI: 10.1038/s41598-017-17330-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 11/10/2017] [Indexed: 01/15/2023] Open
Abstract
Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10-4) alone remained predictive after adjusting for clinical predictors.
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Affiliation(s)
- Rahul Metri
- IISc Mathematics Initiative (IMI), Indian Institute of Science, Bangalore, Karnataka, India
| | - Abhilash Mohan
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India
| | - Jérémie Nsengimana
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Joanna Pozniak
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Carmen Molina-Paris
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
| | - Julia Newton-Bishop
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - David Bishop
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Nagasuma Chandra
- IISc Mathematics Initiative (IMI), Indian Institute of Science, Bangalore, Karnataka, India.
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India.
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38
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Liu G, Wang H, Chu H, Yu J, Zhou X. Functional diversity of topological modules in human protein-protein interaction networks. Sci Rep 2017; 7:16199. [PMID: 29170401 PMCID: PMC5701033 DOI: 10.1038/s41598-017-16270-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 11/09/2017] [Indexed: 01/18/2023] Open
Abstract
A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity
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Affiliation(s)
- Guangming Liu
- School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China
| | - Huixin Wang
- School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China
| | - Hongwei Chu
- Dalian University of Technology, Dalian, 116024, China.,Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Jian Yu
- School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.
| | - Xuezhong Zhou
- School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.
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39
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Quayle AP, Siddiqui AS, Jones SJM. Perturbation of Interaction Networks for Application to Cancer Therapy. Cancer Inform 2017. [DOI: 10.1177/117693510700500005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types. The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.
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40
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Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes. Sci Rep 2017; 7:4896. [PMID: 28687729 PMCID: PMC5501792 DOI: 10.1038/s41598-017-05275-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 05/26/2017] [Indexed: 11/08/2022] Open
Abstract
A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for molecular subtype discovery. In this analysis, we hypothesize that spectral clustering method could identify molecular subtypes in correlation with survival outcomes. We propose an accurate subtype identification method, Cancer Subtype Identification with Spectral Clustering using Nyström approximation (CSISCN), for the discovery of molecular subtypes, based on spectral clustering method. CSISCN could be used to improve gene expression-based identification of breast cancer molecular subtypes. We demonstrated that CSISCN identified the molecular subtypes with distinct clinical outcomes and was valid for the number of molecular subtypes. Furthermore, CSISCN identified molecular subtypes for improving clinical and molecular relevance which significantly outperformed consensus clustering and spectral clustering methods. To test the general applicability of the CSISCN, we further applied it on human CRC datasets and AML datasets and demonstrated superior performance as compared to consensus clustering method. In summary, CSISCN demonstrated the great potential in gene expression-based subtype identification.
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Abstract
Understanding how communities emerge is a fundamental problem in social and economic systems. Here, we experimentally explore the emergence of communities in social networks, using the ultimatum game as a paradigm for capturing individual interactions. We find the emergence of diverse communities in static networks is the result of the local interaction between responders with inherent heterogeneity and rational proposers in which the former act as community leaders. In contrast, communities do not arise in populations with random interactions, suggesting that a static structure stabilizes local communities and social diversity. Our experimental findings deepen our understanding of self-organized communities and of the establishment of social norms associated with game dynamics in social networks. Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.
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Hu L, Chan KCC. Extracting Coevolutionary Features from Protein Sequences for Predicting Protein-Protein Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:155-166. [PMID: 26812730 DOI: 10.1109/tcbb.2016.2520923] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Knowing the ways proteins interact with each other are crucial to our understanding of the functional mechanisms of proteins. It is for this reason that different approaches have been developed in attempts to predict protein-protein interactions (PPIs) computationally. Among them, the sequence-based approaches are preferred to the others as they do not require any information about protein properties to perform their tasks. Instead, most sequence-based approaches make use of feature extraction methods to extract features directly from protein sequences so that for each protein sequence, we can construct a feature vector. The feature vectors of every pair of proteins are then concatenated to form two classes of interacting and non-interacting proteins. The prediction of whether or not two proteins interact with each other is then formulated as a classification problem. How accurate PPI predictions can be made therefore depends on how good the features are that can be extracted from the protein sequences to allow interacting or non-interacting to be best distinguished. To do so, instead of extracting such features from individual protein sequences independently of the other protein in the same pair, we propose to jointly consider features from both sequences in a protein pair during the feature extraction process through using a novel coevolutionary feature extraction approach called CoFex. Coevolutionary features extracted by CoFex refer to the covariations found at coevolving positions. Based on the presence and absence of these coevolutionary features in the sequences of two proteins, feature vectors can be composed for pairs of proteins rather than individual proteins. The experiment results show that CoFex is a promising feature extraction approach and can improve the performance of PPI prediction.
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Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2016; 45:D362-D368. [PMID: 27924014 PMCID: PMC5210637 DOI: 10.1093/nar/gkw937] [Citation(s) in RCA: 4737] [Impact Index Per Article: 592.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Accepted: 10/06/2016] [Indexed: 02/06/2023] Open
Abstract
A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Helen Cook
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alberto Santos
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Alexander Roth
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany .,Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany.,Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Tadaka S, Kinoshita K. NCMine: Core-peripheral based functional module detection using near-clique mining. Bioinformatics 2016; 32:3454-3460. [PMID: 27466623 PMCID: PMC5181566 DOI: 10.1093/bioinformatics/btw488] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 06/28/2016] [Accepted: 07/19/2016] [Indexed: 11/14/2022] Open
Abstract
Motivation: The identification of functional modules from protein–protein interaction (PPI) networks is an important step toward understanding the biological features of PPI networks. The detection of functional modules in PPI networks is often performed by identifying internally densely connected subnetworks, and often produces modules with “core” and “peripheral” proteins. The core proteins are the ones having dense connections to each other in a module. The difference between core and peripheral proteins is important to understand the functional roles of proteins in modules, but there are few methods to explicitly elucidate the internal structure of functional modules at gene level. Results: We propose NCMine, which is a novel network clustering method and visualization tool for the core-peripheral structure of functional modules. It extracts near-complete subgraphs from networks based on a node-weighting scheme using degree centrality, and reports subgroups as functional modules. We implemented this method as a plugin of Cytoscape, which is widely used to visualize and analyze biological networks. The plugin allows users to extract functional modules from PPI networks and interactively filter modules of interest. We applied the method to human PPI networks, and found several examples with the core-peripheral structure of modules that may be related to cancer development. Availability and Implementation: The Cytoscape plugin and tutorial are available at Cytoscape AppStore. (http://apps.cytoscape.org/apps/ncmine). Contact:kengo@ecei.tohoku.ac.jp Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shu Tadaka
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Kengo Kinoshita
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan.,Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.,Tohoku Medical Megabank Organization, Sendai, Japan
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Martin O, Krzywicki A, Zagorski M. Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function. Phys Life Rev 2016; 17:124-58. [DOI: 10.1016/j.plrev.2016.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 03/25/2016] [Accepted: 04/20/2016] [Indexed: 12/23/2022]
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Shen X, Yi L, Jiang X, Zhao Y, Hu X, He T, Yang J. Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network. Methods 2016; 110:90-96. [PMID: 27320204 DOI: 10.1016/j.ymeth.2016.06.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 05/31/2016] [Accepted: 06/14/2016] [Indexed: 12/13/2022] Open
Abstract
Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a protein's neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.
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Affiliation(s)
- Xianjun Shen
- School of Computer, Central China Normal University, Wuhan, China.
| | - Li Yi
- School of Computer, Central China Normal University, Wuhan, China.
| | - Xingpeng Jiang
- School of Computer, Central China Normal University, Wuhan, China.
| | - Yanli Zhao
- School of Computer, Central China Normal University, Wuhan, China.
| | - Xiaohua Hu
- School of Computer, Central China Normal University, Wuhan, China; College of Computing and Informatics, Drexel University, Philadelphia, USA.
| | - Tingting He
- School of Computer, Central China Normal University, Wuhan, China.
| | - Jincai Yang
- School of Computer, Central China Normal University, Wuhan, China.
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Alcalá-Corona SA, Velázquez-Caldelas TE, Espinal-Enríquez J, Hernández-Lemus E. Community Structure Reveals Biologically Functional Modules in MEF2C Transcriptional Regulatory Network. Front Physiol 2016; 7:184. [PMID: 27252657 PMCID: PMC4878384 DOI: 10.3389/fphys.2016.00184] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 05/06/2016] [Indexed: 01/04/2023] Open
Abstract
Gene regulatory networks are useful to understand the activity behind the complex mechanisms in transcriptional regulation. A main goal in contemporary biology is using such networks to understand the systemic regulation of gene expression. In this work, we carried out a systematic study of a transcriptional regulatory network derived from a comprehensive selection of all potential transcription factor interactions downstream from MEF2C, a human transcription factor master regulator. By analyzing the connectivity structure of such network, we were able to find different biologically functional processes and specific biochemical pathways statistically enriched in communities of genes into the network, such processes are related to cell signaling, cell cycle and metabolism. In this way we further support the hypothesis that structural properties of biological networks encode an important part of their functional behavior in eukaryotic cells.
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Affiliation(s)
- Sergio A Alcalá-Corona
- Computational Genomics Department, National Institute of Genomic MedicineMexico City, Mexico; Complexity in Systems Biology, Center for Complexity Sciences, Universidad Nacional Autónoma de MéxicoMexico City, Mexico
| | | | - Jesús Espinal-Enríquez
- Computational Genomics Department, National Institute of Genomic MedicineMexico City, Mexico; Complexity in Systems Biology, Center for Complexity Sciences, Universidad Nacional Autónoma de MéxicoMexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Department, National Institute of Genomic MedicineMexico City, Mexico; Complexity in Systems Biology, Center for Complexity Sciences, Universidad Nacional Autónoma de MéxicoMexico City, Mexico
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Gaulke CA, Barton CL, Proffitt S, Tanguay RL, Sharpton TJ. Triclosan Exposure Is Associated with Rapid Restructuring of the Microbiome in Adult Zebrafish. PLoS One 2016; 11:e0154632. [PMID: 27191725 PMCID: PMC4871530 DOI: 10.1371/journal.pone.0154632] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 04/15/2016] [Indexed: 12/30/2022] Open
Abstract
Growing evidence indicates that disrupting the microbial community that comprises the intestinal tract, known as the gut microbiome, can contribute to the development or severity of disease. As a result, it is important to discern the agents responsible for microbiome disruption. While animals are frequently exposed to a diverse array of environmental chemicals, little is known about their effects on gut microbiome stability and structure. Here, we demonstrate how zebrafish can be used to glean insight into the effects of environmental chemical exposure on the structure and ecological dynamics of the gut microbiome. Specifically, we exposed forty-five adult zebrafish to triclosan-laden food for four or seven days or a control diet, and analyzed their microbial communities using 16S rRNA amplicon sequencing. Triclosan exposure was associated with rapid shifts in microbiome structure and diversity. We find evidence that several operational taxonomic units (OTUs) associated with the family Enterobacteriaceae appear to be susceptible to triclosan exposure, while OTUs associated with the genus Pseudomonas appeared to be more resilient and resistant to exposure. We also found that triclosan exposure is associated with topological alterations to microbial interaction networks and results in an overall increase in the number of negative interactions per microbe in these networks. Together these data indicate that triclosan exposure results in altered composition and ecological dynamics of microbial communities in the gut. Our work demonstrates that because zebrafish afford rapid and inexpensive interrogation of a large number of individuals, it is a useful experimental system for the discovery of the gut microbiome's interaction with environmental chemicals.
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Affiliation(s)
- Christopher A. Gaulke
- Department of Microbiology, Oregon State University, Corvallis, Oregon, United States of America
| | - Carrie L. Barton
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America
- The Environmental Health Sciences Center, Oregon State University, Corvallis, Oregon, United States of America
| | - Sarah Proffitt
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America
- The Environmental Health Sciences Center, Oregon State University, Corvallis, Oregon, United States of America
| | - Robert L. Tanguay
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America
- The Environmental Health Sciences Center, Oregon State University, Corvallis, Oregon, United States of America
| | - Thomas J. Sharpton
- Department of Microbiology, Oregon State University, Corvallis, Oregon, United States of America
- Department of Statistics, Oregon State University, Corvallis, Oregon, United States of America
- * E-mail:
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Arias-Calderón M, Almarza G, Díaz-Vegas A, Contreras-Ferrat A, Valladares D, Casas M, Toledo H, Jaimovich E, Buvinic S. Characterization of a multiprotein complex involved in excitation-transcription coupling of skeletal muscle. Skelet Muscle 2016; 6:15. [PMID: 27069569 PMCID: PMC4827232 DOI: 10.1186/s13395-016-0087-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 03/19/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Electrical activity regulates the expression of skeletal muscle genes by a process known as "excitation-transcription" (E-T) coupling. We have demonstrated that release of adenosine 5'-triphosphate (ATP) during depolarization activates membrane P2X/P2Y receptors, being the fundamental mediators between electrical stimulation, slow intracellular calcium transients, and gene expression. We propose that this signaling pathway would require the proper coordination between the voltage sensor (dihydropyridine receptor, DHPR), pannexin 1 channels (Panx1, ATP release conduit), nucleotide receptors, and other signaling molecules. The goal of this study was to assess protein-protein interactions within the E-T machinery and to look for novel constituents in order to characterize the signaling complex. METHODS Newborn derived myotubes, adult fibers, or triad fractions from rat or mouse skeletal muscles were used. Co-immunoprecipitation, 2D blue native SDS/PAGE, confocal microscopy z-axis reconstruction, and proximity ligation assays were combined to assess the physical proximity of the putative complex interactors. An L6 cell line overexpressing Panx1 (L6-Panx1) was developed to study the influence of some of the complex interactors in modulation of gene expression. RESULTS Panx1, DHPR, P2Y2 receptor (P2Y2R), and dystrophin co-immunoprecipitated in the different preparations assessed. 2D blue native SDS/PAGE showed that DHPR, Panx1, P2Y2R and caveolin-3 (Cav3) belong to the same multiprotein complex. We observed co-localization and protein-protein proximity between DHPR, Panx1, P2Y2R, and Cav3 in adult fibers and in the L6-Panx1 cell line. We found a very restricted location of Panx1 and Cav3 in a putative T-tubule zone near the sarcolemma, while DHPR was highly expressed all along the transverse (T)-tubule. By Panx1 overexpression, extracellular ATP levels were increased both at rest and after electrical stimulation. Basal mRNA levels of the early gene cfos and the oxidative metabolism markers citrate synthase and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) were significantly increased by Panx1 overexpression. Interleukin 6 expression evoked by 20-Hz electrical stimulation (270 pulses, 0.3 ms each) was also significantly upregulated in L6-Panx1 cells. CONCLUSIONS We propose the existence of a relevant multiprotein complex that coordinates events involved in E-T coupling. Unveiling the molecular actors involved in the regulation of gene expression will contribute to the understanding and treatment of skeletal muscle disorders due to wrong-expressed proteins, as well as to improve skeletal muscle performance.
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MESH Headings
- Adenosine Triphosphate/metabolism
- Animals
- Animals, Newborn
- Calcium Channels, L-Type/genetics
- Calcium Channels, L-Type/metabolism
- Caveolin 3/genetics
- Caveolin 3/metabolism
- Cell Line
- Connexins/genetics
- Connexins/metabolism
- Dystrophin/genetics
- Dystrophin/metabolism
- Electric Stimulation
- Gene Expression Regulation
- Mice, Inbred BALB C
- Mice, Inbred C57BL
- Multiprotein Complexes
- Muscle Contraction
- Muscle Fibers, Skeletal/metabolism
- Muscle Proteins/genetics
- Muscle Proteins/metabolism
- Nerve Tissue Proteins/genetics
- Nerve Tissue Proteins/metabolism
- Protein Binding
- Rats, Wistar
- Receptors, Purinergic P2Y2/genetics
- Receptors, Purinergic P2Y2/metabolism
- Transcription, Genetic
- Transcriptional Activation
- Transfection
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Affiliation(s)
- Manuel Arias-Calderón
- />Centro de Estudios Moleculares de la Célula, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
- />Instituto de Investigación en Ciencias Odontológicas, Facultad de Odontología, Universidad de Chile, Sergio Livingstone Pohlhammer 943, 8380492 Santiago, Chile
| | - Gonzalo Almarza
- />Centro de Estudios Moleculares de la Célula, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
| | - Alexis Díaz-Vegas
- />Centro de Estudios Moleculares de la Célula, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
| | - Ariel Contreras-Ferrat
- />Centro de Estudios Moleculares de la Célula, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
| | - Denisse Valladares
- />Centro de Estudios Moleculares de la Célula, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
| | - Mariana Casas
- />Centro de Estudios Moleculares de la Célula, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
| | - Héctor Toledo
- />Programa de Biología Molecular y Celular, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
| | - Enrique Jaimovich
- />Centro de Estudios Moleculares de la Célula, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
- />Programa de Biología Molecular y Celular, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, 8380453 Chile
| | - Sonja Buvinic
- />Instituto de Investigación en Ciencias Odontológicas, Facultad de Odontología, Universidad de Chile, Sergio Livingstone Pohlhammer 943, 8380492 Santiago, Chile
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Banik RS, Rahman MS, Rahman KMT, Islam F, Enayetul Babar SM. Comparison of molecular signatures in large-scale protein interaction networks in normal and cancer conditions of brain, cervix, lung, ovary and prostate. BIOMEDICAL RESEARCH AND THERAPY 2016. [DOI: 10.7603/s40730-016-0018-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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