1
|
Mukhtar MF, Abal Abas Z, Baharuddin AS, Norizan MN, Fakhruddin WFWW, Minato W, Rasib AHA, Abidin ZZ, Rahman AFNA, Anuar SHH. Integrating local and global information to identify influential nodes in complex networks. Sci Rep 2023; 13:11411. [PMID: 37452080 PMCID: PMC10349046 DOI: 10.1038/s41598-023-37570-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 06/23/2023] [Indexed: 07/18/2023] Open
Abstract
Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex networks.
Collapse
Affiliation(s)
| | - Zuraida Abal Abas
- Universiti Teknikal Malaysia Melaka, 76100, Durian Tunggal, Malaysia.
| | | | | | | | | | | | | | | | | |
Collapse
|
2
|
Smith TB, Vacca R, Mantegazza L, Capua I. Discovering new pathways toward integration between health and sustainable development goals with natural language processing and network science. Global Health 2023; 19:44. [PMID: 37386579 DOI: 10.1186/s12992-023-00943-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Research on health and sustainable development is growing at a pace such that conventional literature review methods appear increasingly unable to synthesize all relevant evidence. This paper employs a novel combination of natural language processing (NLP) and network science techniques to address this problem and to answer two questions: (1) how is health thematically interconnected with the Sustainable Development Goals (SDGs) in global science? (2) What specific themes have emerged in research at the intersection between SDG 3 ("Good health and well-being") and other sustainability goals? METHODS After a descriptive analysis of the integration between SDGs in twenty years of global science (2001-2020) as indexed by dimensions.ai, we analyze abstracts of articles that are simultaneously relevant to SDG 3 and at least one other SDG (N = 27,928). We use the top2vec algorithm to discover topics in this corpus and measure semantic closeness between these topics. We then use network science methods to describe the network of substantive relationships between the topics and identify 'zipper themes', actionable domains of research and policy to co-advance health and other sustainability goals simultaneously. RESULTS We observe a clear increase in scientific research integrating SDG 3 and other SDGs since 2001, both in absolute and relative terms, especially on topics relevant to interconnections between health and SDGs 2 ("Zero hunger"), 4 ("Quality education"), and 11 ("Sustainable cities and communities"). We distill a network of 197 topics from literature on health and sustainable development, with 19 distinct network communities - areas of growing integration with potential to further bridge health and sustainability science and policy. Literature focused explicitly on the SDGs is highly central in this network, while topical overlaps between SDG 3 and the environmental SDGs (12-15) are under-developed. CONCLUSION Our analysis demonstrates the feasibility and promise of NLP and network science for synthesizing large amounts of health-related scientific literature and for suggesting novel research and policy domains to co-advance multiple SDGs. Many of the 'zipper themes' identified by our method resonate with the One Health perspective that human, animal, and plant health are closely interdependent. This and similar perspectives will help meet the challenge of 'rewiring' sustainability research to co-advance goals in health and sustainability.
Collapse
Affiliation(s)
- Thomas Bryan Smith
- Bureau of Economic and Business Research, University of Florida, nd Ave Ste 150, PO Box 117148, Gainesville, FL, 32611, USA.
| | - Raffaele Vacca
- Department of Social and Political Sciences, University of Milan, Milan, Italy
| | - Luca Mantegazza
- One Health Center of Excellence, IFAS, University of Florida, Gainesville, FL, USA
| | - Ilaria Capua
- One Health Center of Excellence, IFAS, University of Florida, Gainesville, FL, USA
- Johns Hopkins University, SAIS Europe, Bologna, Italy
| |
Collapse
|
3
|
Sadeghi M, Karimi MR, Karimi AH, Ghorbanpour Farshbaf N, Barzegar A, Schmitz U. Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors. Genes (Basel) 2023; 14:genes14030750. [PMID: 36981021 PMCID: PMC10048224 DOI: 10.3390/genes14030750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this subtype is of vital importance. In this study, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify a differentially expressed co-expression module of mRNAs in EMT-type gastric tumors. Using network analysis and linear discriminant analysis, we identified mRNA motifs and microRNA-based models with strong prognostic and diagnostic relevance: three models comprised of (i) the microRNAs miR-199a-5p and miR-141-3p, (ii) EVC/EVC2/GLI3, and (iii) PDE2A/GUCY1A1/GUCY1B1 gene expression profiles distinguish EMT-type tumors from other gastric tumors with high accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.995, AUC = 0.9742, and AUC = 0.9717; respectively). Additionally, the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (hazard ratio > 3). Molecular functions of the members of the identified models highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of gastric cancer and underscore their potential utility in precision therapeutic approaches.
Collapse
Affiliation(s)
- Mehdi Sadeghi
- Department of Cell & Molecular Biology, Semnan University, Semnan 3513119111, Iran
| | - Mohammad Reza Karimi
- Department of Cell & Molecular Biology, Semnan University, Semnan 3513119111, Iran
| | - Amir Hossein Karimi
- Department of Cell & Molecular Biology, Semnan University, Semnan 3513119111, Iran
| | | | - Abolfazl Barzegar
- Department of Biology, Faculty of Natural Science, University of Tabriz, Tabriz 5166616471, Iran
| | - Ulf Schmitz
- Department of Molecular & Cell Biology, James Cook University, Townsville, QLD 4811, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD 4878, Australia
| |
Collapse
|
4
|
Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis. Int J Mol Sci 2023; 24:ijms24043075. [PMID: 36834486 PMCID: PMC9965660 DOI: 10.3390/ijms24043075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
Glioblastoma multiforme (GBM), a grade IV glioma, is a challenging disease for patients and clinicians, with an extremely poor prognosis. These tumours manifest a high molecular heterogeneity, with limited therapeutic options for patients. Since GBM is a rare disease, sufficient statistically strong evidence is often not available to explore the roles of lesser-known GBM proteins. We present a network-based approach using centrality measures to explore some key, topologically strategic proteins for the analysis of GBM. Since network-based analyses are sensitive to changes in network topology, we analysed nine different GBM networks, and show that small but well-curated networks consistently highlight a set of proteins, indicating their likely involvement in the disease. We propose 18 novel candidates which, based on differential expression, mutation analysis, and survival analysis, indicate that they may play a role in GBM progression. These should be investigated further for their functional roles in GBM, their clinical prognostic relevance, and their potential as therapeutic targets.
Collapse
|
5
|
Abstract
Proteins are structural and functional components of cells. They interact with each other to drive specific cellular functions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologically important proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.
Collapse
Affiliation(s)
- Vijaykumar Yogesh Muley
- Independent Researcher, Jijamata Nagar, Hingoli, India.
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México.
| |
Collapse
|
6
|
Salihoglu R, Srivastava M, Liang C, Schilling K, Szalay A, Bencurova E, Dandekar T. PRO-Simat: Protein network simulation and design tool. Comput Struct Biotechnol J 2023; 21:2767-2779. [PMID: 37181657 PMCID: PMC10172639 DOI: 10.1016/j.csbj.2023.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023] Open
Abstract
PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.
Collapse
Affiliation(s)
- Rana Salihoglu
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany
| | - Mugdha Srivastava
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany
- Core Unit Systems Medicine, University of Würzburg, 97080 Würzburg, Germany
| | - Chunguang Liang
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany
| | - Klaus Schilling
- Informatics VII, Robotics and Telematics, Department of Mathematics and Informatics, Am Hubland, University of Würzburg, D-97074 Würzburg, Germany
| | - Aladar Szalay
- Dept. of Biochemistry, Biocenter, Am Hubland, University of Würzburg, D-97074 Würzburg, Germany
- Department of Radiation Medicine and Applied Sciences, Rebecca & John Moores Comprehensive Cancer Center, University of California, San Diego, CA, USA
- Dept. of Pathology, Center of Immune technologies, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Elena Bencurova
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany
- Corresponding author.
| | - Thomas Dandekar
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany
- Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
- Corresponding author at: Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany.
| |
Collapse
|
7
|
Ginsberg SD, Sharma S, Norton L, Chiosis G. Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes. Trends Pharmacol Sci 2023; 44:20-33. [PMID: 36414432 PMCID: PMC9789192 DOI: 10.1016/j.tips.2022.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/21/2022]
Abstract
Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.
Collapse
Affiliation(s)
- Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Sahil Sharma
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA
| | - Larry Norton
- Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Gabriela Chiosis
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA; Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| |
Collapse
|
8
|
Kumar N, Mukhtar S. Building Protein-Protein Interaction Graph Database Using Neo4j. Methods Mol Biol 2023; 2690:469-479. [PMID: 37450167 DOI: 10.1007/978-1-0716-3327-4_36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
A cell's various components interact with each other in a coordinated manner to respond to environmental cues and intracellular signals. Compared to the other biological networks, the protein-protein interaction (PPI) is mostly responsible for maintaining signaling pathways. Increasing numbers of experimentally verified and predicted PPIs in plants demand a scalable platform to deal with large and complex datasets. Network/graph data can be organized and analyzed using different tools. This chapter uses Neo4j, a graph database management system, to store and analyze plant PPI networks. To make the graph database and analyze network centrality, we used Arabidopsis interactome-1 main (AI-1MAIN) PPI network.
Collapse
Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
| |
Collapse
|
9
|
Ascoli C, Schott CA, Huang Y, Turturice BA, Wang W, Ecanow N, Sweiss NJ, Perkins DL, Finn PW. Altered transcription factor targeting is associated with differential peripheral blood mononuclear cell proportions in sarcoidosis. Front Immunol 2022; 13:848759. [PMID: 36311769 PMCID: PMC9608777 DOI: 10.3389/fimmu.2022.848759] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionIn sarcoidosis, peripheral lymphopenia and anergy have been associated with increased inflammation and maladaptive immune activity, likely promoting development of chronic and progressive disease. However, the molecular mechanisms that lead to reduced lymphocyte proportions, particularly CD4+ T-cells, have not been fully elucidated. We posit that paradoxical peripheral lymphopenia is characterized by a dysregulated transcriptomic network associated with cell function and fate that results from altered transcription factor targeting activity.MethodsMessenger RNA-sequencing (mRNA-seq) was performed on peripheral blood mononuclear cells (PBMCs) from ACCESS study subjects with sarcoidosis and matched controls and findings validated on a sarcoidosis case-control cohort and a sarcoidosis case series. Preserved PBMC transcriptomic networks between case-control cohorts were assessed to establish cellular associations with gene modules and define regulatory targeting involved in sarcoidosis immune dysregulation utilizing weighted gene co-expression network analysis and differential transcription factor involvement analysis. Network centrality measures identified master transcriptional regulators of subnetworks related to cell proliferation and death. Predictive models of differential PBMC proportions constructed from ACCESS target gene expression corroborated the relationship between aberrant transcription factor regulatory activity and imputed and clinical PBMC populations in the validation cohorts.ResultsWe identified two unique and preserved gene modules significantly associated with sarcoidosis immune dysregulation. Strikingly, increased expression of a monocyte-driven, and not a lymphocyte-driven, gene module related to innate immunity and cell death was the best predictor of peripheral CD4+ T-cell proportions. Within the gene network of this monocyte-driven module, TLE3 and CBX8 were determined to be master regulators of the cell death subnetwork. A core gene signature of differentially over-expressed target genes of TLE3 and CBX8 involved in cellular communication and immune response regulation accurately predicted imputed and clinical monocyte expansion and CD4+ T-cell depletion.ConclusionsAltered transcriptional regulation associated with aberrant gene expression of a monocyte-driven transcriptional network likely influences lymphocyte function and survival. Although further investigation is warranted, this indicates that crosstalk between hyperactive monocytes and lymphocytes may instigate peripheral lymphopenia and underlie sarcoidosis immune dysregulation and pathogenesis. Future therapies selectively targeting master regulators, or their targets, may mitigate dysregulated immune processes in sarcoidosis and disease progression.
Collapse
Affiliation(s)
- Christian Ascoli
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Cody A. Schott
- University of Illinois at Chicago College of Medicine, Chicago, IL, United States
| | - Yue Huang
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | | | - Wangfei Wang
- Department of Bioengineering, University of Illinois at Chicago College of Engineering and Medicine, Chicago, IL, United States
| | - Naomi Ecanow
- University of Illinois at Chicago College of Medicine, Chicago, IL, United States
| | - Nadera J. Sweiss
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
- Division of Rheumatology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - David L. Perkins
- Division of Nephrology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Patricia W. Finn
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
- *Correspondence: Patricia W. Finn,
| |
Collapse
|
10
|
Ahmed MM, Shafat Z, Tazyeen S, Ali R, Almashjary MN, Al-Raddadi R, Harakeh S, Alam A, Haque S, Ishrat R. Identification of pathogenic genes associated with CKD: An integrated bioinformatics approach. Front Genet 2022; 13:891055. [PMID: 36035163 PMCID: PMC9403320 DOI: 10.3389/fgene.2022.891055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 06/28/2022] [Indexed: 11/23/2022] Open
Abstract
Chronic kidney disease (CKD) is defined as a persistent abnormality in the structure and function of kidneys and leads to high morbidity and mortality in individuals across the world. Globally, approximately 8%–16% of the population is affected by CKD. Proper screening, staging, diagnosis, and the appropriate management of CKD by primary care clinicians are essential in preventing the adverse outcomes associated with CKD worldwide. In light of this, the identification of biomarkers for the appropriate management of CKD is urgently required. Growing evidence has suggested the role of mRNAs and microRNAs in CKD, however, the gene expression profile of CKD is presently uncertain. The present study aimed to identify diagnostic biomarkers and therapeutic targets for patients with CKD. The human microarray profile datasets, consisting of normal samples and treated samples were analyzed thoroughly to unveil the differentially expressed genes (DEGs). After selection, the interrelationship among DEGs was carried out to identify the overlapping DEGs, which were visualized using the Cytoscape program. Furthermore, the PPI network was constructed from the String database using the selected DEGs. Then, from the PPI network, significant modules and sub-networks were extracted by applying the different centralities methods (closeness, betweenness, stress, etc.) using MCODE, Cytohubba, and Centiserver. After sub-network analysis we identified six overlapped hub genes (RPS5, RPL37A, RPLP0, CXCL8, HLA-A, and ANXA1). Additionally, the enrichment analysis was undertaken on hub genes to determine their significant functions. Furthermore, these six genes were used to find their associated miRNAs and targeted drugs. Finally, two genes CXCL8 and HLA-A were common for Ribavirin drug (the gene-drug interaction), after docking studies HLA-A was selected for further investigation. To conclude our findings, we can say that the identified hub genes and their related miRNAs can serve as potential diagnostic biomarkers and therapeutic targets for CKD treatment strategies.
Collapse
Affiliation(s)
- Mohd Murshad Ahmed
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Zoya Shafat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Safia Tazyeen
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Rafat Ali
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
- Department of Biosciences, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Majed N. Almashjary
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rajaa Al-Raddadi
- Community Medicine Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Steve Harakeh
- King Fahd Medical Research Center, and Yousef Abdullatif Jameel Chair of Prophetic Medicine Application, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Aftab Alam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
- *Correspondence: Romana Ishrat,
| |
Collapse
|
11
|
O'Donnell MS, Edmunds DR, Aldridge CL, Heinrichs JA, Monroe AP, Coates PS, Prochazka BG, Hanser SE, Wiechman LA. Defining fine‐scaled population structure among continuously distributed populations. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - David R. Edmunds
- U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA
| | - Cameron L. Aldridge
- U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA
| | - Julie A. Heinrichs
- Natural Resource Ecology Laboratory Colorado State University, Fort Collins, CO in cooperation with the U.S. Geological Survey, Fort Collins Science Center Fort Collins Colorado USA
| | - Adrian P. Monroe
- U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA
| | - Peter S. Coates
- U.S. Geological Survey, Western Ecological Research Center Dixon Field Station Dixon California USA
| | - Brian G. Prochazka
- U.S. Geological Survey, Western Ecological Research Center Dixon Field Station Dixon California USA
| | - Steve E. Hanser
- U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA
| | - Lief A. Wiechman
- U.S. Geological Survey Ecosystems Mission Area Fort Collins Colorado USA
| |
Collapse
|
12
|
Rawls E, Kummerfeld E, Mueller BA, Ma S, Zilverstand A. The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks. Neuroimage 2022; 255:119211. [PMID: 35430360 PMCID: PMC9177236 DOI: 10.1016/j.neuroimage.2022.119211] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023] Open
Abstract
We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.
Collapse
Affiliation(s)
- Eric Rawls
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, USA.
| | | | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, USA
| | - Sisi Ma
- Institute for Health Informatics, University of Minnesota, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, USA; Medical Discovery Team on Addiction, University of Minnesota, USA
| |
Collapse
|
13
|
Manchado-Gobatto FB, Torres RS, Marostegan AB, Rasteiro FM, Hartz CS, Moreno MA, Pinto AS, Gobatto CA. Complex Network Model Reveals the Impact of Inspiratory Muscle Pre-Activation on Interactions among Physiological Responses and Muscle Oxygenation during Running and Passive Recovery. BIOLOGY 2022; 11:biology11070963. [PMID: 36101345 PMCID: PMC9311794 DOI: 10.3390/biology11070963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/11/2022] [Accepted: 06/14/2022] [Indexed: 12/05/2022]
Abstract
Simple Summary Different warm-ups can be used to improve physical and sports performance. Among these strategies, we can include the pre-activation of the inspiratory muscles. Our study aimed to investigate this pre-activation model in high-intensity running performance and recovery using an integrative computational analysis called a complex network. The participants in this study underwent four sessions. The first and second sessions were performed to explain the procedures, characterize them and determine the individualized pre-activation intensity (40% of the maximum inspiratory pressure). Subsequently, on different days, the subjects were submitted to high-intensity tethered runs on a non-motorized treadmill with monitoring of the physiological responses during and after this effort. To understand the impacts of the pre-activation of inspiratory muscles on the organism, we studied the centrality metrics obtained by complex networks, which help in the interpretation of data in a more integrated way. Our results revealed that the graphs generated by this analysis were altered when inspiratory muscle pre-activation was applied, emphasizing muscle oxygenation responses in the leg and arm. Blood lactate also played an important role, especially after our inspiratory muscle strategy. Our findings confirm that the pre-activation of inspiratory muscles promotes modulations in the organism, better integrating physiological responses, which could increase performance and improve recovery. Abstract Although several studies have focused on the adaptations provided by inspiratory muscle (IM) training on physical demands, the warm-up or pre-activation (PA) of these muscles alone appears to generate positive effects on physiological responses and performance. This study aimed to understand the effects of inspiratory muscle pre-activation (IMPA) on high-intensity running and passive recovery, as applied to active subjects. In an original and innovative investigation of the impacts of IMPA on high-intensity running, we proposed the identification of the interactions among physical characteristics, physiological responses and muscle oxygenation in more and less active muscle to a running exercise using a complex network model. For this, fifteen male subjects were submitted to all-out 30 s tethered running efforts preceded or not preceded by IMPA, composed of 2 × 15 repetitions (1 min interval between them) at 40% of the maximum individual inspiratory pressure using a respiratory exercise device. During running and recovery, we monitored the physiological responses (heart rate, blood lactate, oxygen saturation) and muscle oxygenation (in vastus lateralis and biceps brachii) by wearable near-infrared spectroscopy (NIRS). Thus, we investigated four scenarios: two in the tethered running exercise (with or without IMPA) and two built into the recovery process (after the all-out 30 s), under the same conditions. Undirected weighted graphs were constructed, and four centrality metrics were analyzed (Degree, Betweenness, Eigenvector, and Pagerank). The IMPA (40% of the maximum inspiratory pressure) was effective in increasing the peak and mean relative running power, and the analysis of the complex networks advanced the interpretation of the effects of physiological adjustments related to the IMPA on exercise and recovery. Centrality metrics highlighted the nodes related to muscle oxygenation responses (in more and less active muscles) as significant to all scenarios, and systemic physiological responses mediated this impact, especially after IMPA application. Our results suggest that this respiratory strategy enhances exercise, recovery and the multidimensional approach to understanding the effects of physiological adjustments on these conditions.
Collapse
Affiliation(s)
- Fúlvia Barros Manchado-Gobatto
- Laboratory of Applied Sport Physiology, School of Applied Sciences, University of Campinas, Limeira 13484-350, Brazil; (A.B.M.); (F.M.R.); (C.A.G.)
- Correspondence:
| | - Ricardo Silva Torres
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 6009 Ålesund, Norway;
| | - Anita Brum Marostegan
- Laboratory of Applied Sport Physiology, School of Applied Sciences, University of Campinas, Limeira 13484-350, Brazil; (A.B.M.); (F.M.R.); (C.A.G.)
| | - Felipe Marroni Rasteiro
- Laboratory of Applied Sport Physiology, School of Applied Sciences, University of Campinas, Limeira 13484-350, Brazil; (A.B.M.); (F.M.R.); (C.A.G.)
| | - Charlini Simoni Hartz
- Postgraduate Program in Human Movement Sciences, Methodist University of Piracicaba, Piracicaba 13400-000, Brazil; (C.S.H.); (M.A.M.)
| | - Marlene Aparecida Moreno
- Postgraduate Program in Human Movement Sciences, Methodist University of Piracicaba, Piracicaba 13400-000, Brazil; (C.S.H.); (M.A.M.)
| | - Allan Silva Pinto
- Department of Sport Sciences, Faculty of Physical Education, University of Campinas, Campinas 13083-851, Brazil;
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-970, Brazil
| | - Claudio Alexandre Gobatto
- Laboratory of Applied Sport Physiology, School of Applied Sciences, University of Campinas, Limeira 13484-350, Brazil; (A.B.M.); (F.M.R.); (C.A.G.)
| |
Collapse
|
14
|
Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci 2022. [PMID: 36210749 PMCID: PMC9018971 DOI: 10.1007/s12038-022-00253-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
Collapse
Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Rupa Bhowmick
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Chandrakala Meena
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| |
Collapse
|
15
|
Khojasteh H, Khanteymoori A, Olyaee MH. Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features. Sci Rep 2022; 12:5867. [PMID: 35393450 PMCID: PMC8988119 DOI: 10.1038/s41598-022-08574-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 03/03/2022] [Indexed: 01/04/2023] Open
Abstract
SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.
Collapse
Affiliation(s)
- Hakimeh Khojasteh
- Department of Computer Engineering, University of Zanjan, Zanjan, Iran
| | | | - Mohammad Hossein Olyaee
- Department of Computer Engineering, Engineering Faculty, University of Gonabad, Zanjan, Gonabad, Iran
| |
Collapse
|
16
|
Vega Magdaleno GD, Bespalov V, Zheng Y, Freitas AA, de Magalhaes JP. Machine learning-based predictions of dietary restriction associations across ageing-related genes. BMC Bioinformatics 2022; 23:10. [PMID: 34983372 PMCID: PMC8729156 DOI: 10.1186/s12859-021-04523-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 12/08/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Dietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related genes and DR-related genetic features. RESULTS This work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking DR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict DR-relatedness among ageing-related genes currently lacking DR-related annotations in the data, resulting in a set of promising candidate DR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted DR-relatedness remain to be validated in future wet-lab experiments. CONCLUSIONS This work demonstrated the strong potential of ML-based techniques to identify DR-associated features as our findings are consistent with literature and recent discoveries. Although the inference of new DR-related mechanistic findings based solely on GO terms and biological pathways was limited due to their knowledge-driven nature, the predictive power of these two features types remained useful as it allowed inferring new promising candidate DR-related genes.
Collapse
Affiliation(s)
- Gustavo Daniel Vega Magdaleno
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby St, Liverpool, L7 8TX, UK
| | - Vladislav Bespalov
- School of Computer Technologies and Controls, ITMO University, Kronverkskiy Prospekt 49, 197101, St Petersburg, Russia
| | - Yalin Zheng
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby St, Liverpool, L7 8TX, UK
| | - Alex A Freitas
- School of Computing, University of Kent, Canterbury, CT2 7NF, UK
| | - Joao Pedro de Magalhaes
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby St, Liverpool, L7 8TX, UK.
| |
Collapse
|
17
|
Masasa M, Kushmaro A, Kramarsky-Winter E, Shpigel M, Barkan R, Golberg A, Kribus A, Shashar N, Guttman L. Mono-specific algal diets shape microbial networking in the gut of the sea urchin Tripneustes gratilla elatensis. Anim Microbiome 2021; 3:79. [PMID: 34782025 PMCID: PMC8594234 DOI: 10.1186/s42523-021-00140-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Algivorous sea urchins can obtain energy from a diet of a single algal species, which may result in consequent changes in their gut microbe assemblies and association networks. METHODS To ascertain whether such changes are led by specific microbes or limited to a specific region in the gut, we compared the microbial assembly in the three major gut regions of the sea urchin Tripneustes gratilla elatensis when fed a mono-specific algal diet of either Ulva fasciata or Gracilaria conferta, or an algal-free diet. DNA extracts from 5 to 7 individuals from each diet treatment were used for Illumina MiSeq based 16S rRNA gene sequencing (V3-V4 region). Niche breadth of each microbe in the assembly was calculated for identification of core, generalist, specialist, or unique microbes. Network analyzers were used to measure the connectivity of the entire assembly and of each of the microbes within it and whether it altered with a given diet or gut region. Lastly, the predicted metabolic functions of key microbes in the gut were analyzed to evaluate their potential contribution to decomposition of dietary algal polysaccharides. RESULTS Sea urchins fed with U. fasciata grew faster and their gut microbiome network was rich in bacterial associations (edges) and networking clusters. Bacteroidetes was the keystone microbe phylum in the gut, with core, generalist, and specialist representatives. A few microbes of this phylum were central hub nodes that maintained community connectivity, while others were driver microbes that led the rewiring of the assembly network based on diet type through changes in their associations and centrality. Niche breadth agreed with microbes' richness in genes for carbohydrate active enzymes and correlated Bacteroidetes specialists to decomposition of specific polysaccharides in the algal diets. CONCLUSIONS The dense and well-connected microbial network in the gut of Ulva-fed sea urchins, together with animal's rapid growth, may suggest that this alga was most nutritious among the experimental diets. Our findings expand the knowledge on the gut microbial assembly in T. gratilla elatensis and strengthen the correlation between microbes' generalism or specialism in terms of occurrence in different niches and their metabolic arsenal which may aid host nutrition.
Collapse
Affiliation(s)
- Matan Masasa
- Marine Biology and Biotechnology Program, Department of Life Sciences, Ben-Gurion University of the Negev, Eilat Campus, Eilat, Israel.,Israel Oceanographic and Limnological Research, The National Center for Mariculture, P.O. Box 1212, 8811201, Eilat, Israel
| | - Ariel Kushmaro
- Avram and Stella Goldstein-Goren, Department of Biotechnology Engineering, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel
| | - Esti Kramarsky-Winter
- Avram and Stella Goldstein-Goren, Department of Biotechnology Engineering, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel
| | - Muki Shpigel
- Morris Kahn Marine Research Station, The Leon H. Charney School of Marine Sciences, University of Haifa, 3498838, Haifa, Israel
| | - Roy Barkan
- Marine Biology and Biotechnology Program, Department of Life Sciences, Ben-Gurion University of the Negev, Eilat Campus, Eilat, Israel.,Israel Oceanographic and Limnological Research, The National Center for Mariculture, P.O. Box 1212, 8811201, Eilat, Israel
| | - Alex Golberg
- Department of Environmental Studies, Tel Aviv University, P.O. Box 39040, 6997801, Tel Aviv, Israel
| | - Abraham Kribus
- School of Mechanical Engineering, Tel Aviv University, P.O. Box 39040, 6997801, Tel Aviv, Israel
| | - Nadav Shashar
- Marine Biology and Biotechnology Program, Department of Life Sciences, Ben-Gurion University of the Negev, Eilat Campus, Eilat, Israel
| | - Lior Guttman
- Israel Oceanographic and Limnological Research, The National Center for Mariculture, P.O. Box 1212, 8811201, Eilat, Israel.
| |
Collapse
|
18
|
Windsor FM, Tavella J, Rother DC, Raimundo RLG, Devoto M, Guimarães PR, Evans DM. Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks. J Appl Ecol 2021. [DOI: 10.1111/1365-2664.14007] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Fredric M. Windsor
- School of Natural and Environmental Sciences Newcastle University Newcastle upon Tyne UK
| | - Julia Tavella
- Facultad de Agronomía Universidad de Buenos Aires Buenos Aires Argentina
| | - Débora C. Rother
- Departamento de Ecologia Universidade de São Paulo São Paulo Brazil
| | - Rafael L. G. Raimundo
- Departamento de Engenharia e Meio Ambiente Universidade Federal da Paraíba Joao Pessoa Brazil
| | - Mariano Devoto
- Facultad de Agronomía Universidad de Buenos Aires Buenos Aires Argentina
| | | | - Darren M. Evans
- School of Natural and Environmental Sciences Newcastle University Newcastle upon Tyne UK
| |
Collapse
|
19
|
Soares GH, Santiago PHR, Biazevic MGH, Michel-Crosato E, Jamieson L. Do network centrality measures predict dental outcomes of Indigenous children over time? Int J Paediatr Dent 2021; 31:634-646. [PMID: 33222405 DOI: 10.1111/ipd.12749] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 10/18/2020] [Accepted: 11/03/2020] [Indexed: 02/01/2023]
Abstract
BACKGROUND Centrality measures identify items that are central to a network, which may inform potential targets for oral interventions. AIM We tested whether centrality measures in a cross-sectional network of mothers' baseline factors are able to predict the association with children's dental outcomes at age 5 years. DESIGN A network approach was applied to longitudinal data from a randomised controlled trial of dental caries prevention delivered to 448 women pregnant with an Indigenous child in South Australia. Central items were identified at baseline using three centrality measures (strength, betweenness, and closeness). Centrality values of mothers' outcomes were regressed with their predictive values to dental caries experience and dental service utilisation at child age 5 years. RESULTS Items of oral health self-efficacy and oral health literacy were central to mothers' baseline network. Strength at baseline explained 51% and 45% of items' predictive values to dental caries experience and dental service utilisation at child age 5 years, respectively. Adjusted and unadjusted values of node strength for the children's oral health network were highly correlated. CONCLUSION Strength at baseline successfully identified mothers' items with greater importance to dental caries experience and dental service utilisation at child age 5 years.
Collapse
Affiliation(s)
| | | | | | | | - Lisa Jamieson
- Australian Research Centre for Population Oral Health, The University of Adelaide, Adelaide, SA, Australia
| |
Collapse
|
20
|
Badkas A, De Landtsheer S, Sauter T. Topological network measures for drug repositioning. Brief Bioinform 2021; 22:bbaa357. [PMID: 33348366 PMCID: PMC8294518 DOI: 10.1093/bib/bbaa357] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/03/2020] [Accepted: 11/05/2020] [Indexed: 12/15/2022] Open
Abstract
Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.
Collapse
|
21
|
Manibalan S, Harison Raj AB, Achary A. Screening of Atherosclerotic Druggable Targets from the Proteome Network of Differentially Expressed Genes. Assay Drug Dev Technol 2021; 19:290-299. [PMID: 34171974 DOI: 10.1089/adt.2021.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Differently expressed genes of atherosclerotic sample analysis are helpful to sort the prominent genes that influence the plaque formation and progression. Scientific evidence-based protein-protein interaction network (PPIN) studies were used to find hub proteins in complex disease conditions. Druggable capacity is one of the important parameters to confirm as a successful drug target. Construction of protein interaction network and principal node analysis (PNA) on atherosclerotic data sets lead to screen the hub proteins. Furthermore, druggable property of protein pocket confirms the targetability of susceptible target candidates and for target selection. Differentially expressed genes are identified through GEO2R analyzer on data sets of various atherosclerotic samples. STRING database and Cytoscape are employed to construct PPIN. Targets were identified by PNA such as centrality measures and clustering algorithm. Gene Ontology enrichment also used as one of the screening parameters to filter the candidates related to atherosclerotic terms. Topological evaluation of target protein was successfully done by ITASSER and GROMACS, respectively. Grid-based principle of DoGSiteScorer is utilized for druggability analysis. Six proteins such as integrin alpha L (ITGAL), metallothionein 1F (MT1F), metallothionein 1X (MT1X), P-selectin glycoprotein ligand-1 (SELPLG), solute carrier family 30 A, zinc transporter protein (SLC30A1), and TNFSF13B are screened as potential biomarkers through network-based analysis. Among the six, ITGAL, SELPLG, SLC30A1, and TNSF13B are identified as better prioritized atherosclerotic targets through druggability efficiency.
Collapse
Affiliation(s)
- Subramaniyan Manibalan
- Centre for Research, Department of Biotechnology, Kamaraj College of Engineering and Technology, Madurai, India
| | | | - Anant Achary
- Centre for Research, Department of Biotechnology, Kamaraj College of Engineering and Technology, Madurai, India
| |
Collapse
|
22
|
Mikryukov VS, Dulya OV, Likhodeevskii GA, Vorobeichik EL. Analysis of Ecological Networks in Multicomponent Communities of Microorganisms: Possibilities, Limitations, and Potential Errors. RUSS J ECOL+ 2021. [DOI: 10.1134/s1067413621030085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
23
|
Gündüç S, Eryiğit R. Time dependent correlations between the probability of a node being infected and its centrality measures. PHYSICA A 2021; 563:125483. [PMID: 33106728 PMCID: PMC7577260 DOI: 10.1016/j.physa.2020.125483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/03/2020] [Indexed: 06/11/2023]
Abstract
Pandemics are a growing world-wide threat for all societies. Throughout history, various infectious diseases presented widely spread damage to human life, economic viability and general well-being. The scale of destruction of the most recent pandemic, COVID-19, has yet to be seen. This work aims to introduce intervention methodology for the prevention of global scale spread of infectious diseases. The proposed method combines time-dependent infection spreading data with the social connectivity structure of the society. SIR model simulations provided the dynamic of contamination spread in different sets of network data. Seven centrality measures parameterized the local and global importance of each node in the underlying network. At each time step the calculated values of the correlations between node infection probability and node centrality values are analyzed. Calculations show that correlations increase at the beginning of infection spread and reaches its highest value when spreading starts to become an epidemic. The peak is at the very early stages of the spreading; and with this analysis, it is possible to predict the node infection probability from time-dependent correlations data.
Collapse
Affiliation(s)
- Semra Gündüç
- Ankara University Computer Engineering Department, Ankara, Turkey
| | - Recep Eryiğit
- Ankara University Computer Engineering Department, Ankara, Turkey
| |
Collapse
|
24
|
Vignery K, Laurier W. A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks? PLoS One 2020; 15:e0244377. [PMID: 33378341 PMCID: PMC7773201 DOI: 10.1371/journal.pone.0244377] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 12/08/2020] [Indexed: 01/18/2023] Open
Abstract
In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes' centrality within their network(s) of interest. In the literature, the most frequent measures used are degree, closeness and/or betweenness centrality, even if other measures might be valid candidates for representing the importance of nodes within networks. The main contribution of this paper is the development of a methodology that allows one to understand, compare and validate centrality indices when studying a particular network of interest. The proposed methodology integrates the following steps: choosing the centrality measures for the network of interest; developing a theoretical taxonomy of these measures; identifying, by means of Principal Component Analysis (PCA), latent dimensions of centrality within the network of interest; verifying the proposed taxonomy of centrality measures; and identifying the centrality measures that best represent the network of interest. Also, we applied the proposed methodology to an existing graph of interest, in our case a real friendship student network. We chose eighteen centrality measures that were developed in SNA and are available and computed in a specific library (CINNA), defined them thoroughly, and proposed a theoretical taxonomy of these eighteen measures. PCA showed the emergence of six latent dimensions of centrality within the student network and saturation of most of the centrality indices on the same categories as those proposed by the theoretical taxonomy. Additionally, the results suggest that indices other than the ones most frequently applied might be more relevant for research on friendship student networks. Finally, the integrated methodology that we propose can be applied to other centrality indices and/or other network types than student graphs.
Collapse
Affiliation(s)
- Kristel Vignery
- Department of Economics & Management, Université Saint-Louis—Bruxelles, Brussels, Belgium
| | - Wim Laurier
- Department of Economics & Management, Université Saint-Louis—Bruxelles, Brussels, Belgium
| |
Collapse
|
25
|
Rakhsh-Khorshid H, Samimi H, Torabi S, Sajjadi-Jazi SM, Samadi H, Ghafouri F, Asgari Y, Haghpanah V. Network analysis reveals essential proteins that regulate sodium-iodide symporter expression in anaplastic thyroid carcinoma. Sci Rep 2020; 10:21440. [PMID: 33293661 PMCID: PMC7722919 DOI: 10.1038/s41598-020-78574-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/18/2020] [Indexed: 12/31/2022] Open
Abstract
Anaplastic thyroid carcinoma (ATC) is the most rare and lethal form of thyroid cancer and requires effective treatment. Efforts have been made to restore sodium-iodide symporter (NIS) expression in ATC cells where it has been downregulated, yet without complete success. Systems biology approaches have been used to simplify complex biological networks. Here, we attempt to find more suitable targets in order to restore NIS expression in ATC cells. We have built a simplified protein interaction network including transcription factors and proteins involved in MAPK, TGFβ/SMAD, PI3K/AKT, and TSHR signaling pathways which regulate NIS expression, alongside proteins interacting with them. The network was analyzed, and proteins were ranked based on several centrality indices. Our results suggest that the protein interaction network of NIS expression regulation is modular, and distance-based and information-flow-based centrality indices may be better predictors of important proteins in such networks. We propose that the high-ranked proteins found in our analysis are expected to be more promising targets in attempts to restore NIS expression in ATC cells.
Collapse
Affiliation(s)
- Hassan Rakhsh-Khorshid
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.,Apoptosis Research Centre, National University of Ireland, Galway, Ireland
| | - Hilda Samimi
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Dr. Shariati Hospital, North Kargar Ave, Tehran, 14114, Iran
| | - Shukoofeh Torabi
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran
| | - Sayed Mahmoud Sajjadi-Jazi
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Dr. Shariati Hospital, North Kargar Ave, Tehran, 14114, Iran.,Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamed Samadi
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Dr. Shariati Hospital, North Kargar Ave, Tehran, 14114, Iran
| | - Fatemeh Ghafouri
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Dr. Shariati Hospital, North Kargar Ave, Tehran, 14114, Iran.,Department of Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Yazdan Asgari
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Italia St., Tehran, 1417755469, Iran.
| | - Vahid Haghpanah
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Dr. Shariati Hospital, North Kargar Ave, Tehran, 14114, Iran. .,Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
26
|
Chemical constituents and gastro-protective potential of Pachira glabra leaves against ethanol-induced gastric ulcer in experimental rat model. Inflammopharmacology 2020; 29:317-332. [PMID: 32914383 DOI: 10.1007/s10787-020-00749-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 08/25/2020] [Indexed: 12/18/2022]
Abstract
Gastric ulcer is a very common illness that adversely affects a significant number of people all over the globe. Phytochemical investigation of P. glabra leaf alcohol extract (PGLE) resulted in the isolation and Characterization of a new nature compound, quercetin-3- O-α -L-rhamnosyl-(1'''-6'')-(4''- O -acetyl)-β -D-galactoside (4), in addition to seven known compounds. They are ferulic acid (1), p- coumaric acid (2), quercetin 3-O-α-L-rhamnoside-3'-O-β-D-glucoside (3), quercetin-3- O-α -L-rhamnosyl-(1'''-6'')-(4''- O -acetyl)- β -Dgalactoside (4), quercetin-3- O-β -D-galactoside (5), 7-hydroxy maltol-3-O-β-D-glucoside (6), maltol-3- O-β -D-glucoside (7), and methyl coumarate (8) that were first to be isolated from the genus Pachira. PGLE demonstrated in vitro anti-Helicobacter pylori activity. Moreover, the in vivo gastroprotective assessment of PGLE at different dosses, 100, 200, and 400 mg/kg against ethanol induced ulceration revealed a dose-dependent gastroprotection comparable to omeprazole. PGLE attenuated gastric lesions and histopathological changes triggered by ethanol. Interestingly, PGLE exhibited an anti-inflammatory effect through down-regulating the expression of nuclear factor-ĸB and pro-inflammatory enzyme cyclooxygenase-2 in the ulcer group. It also hindered apoptosis through decreasing Bax and increasing Bcl-2 expression hence decreasing Bax/Bcl2 ratio with a subsequent reduction in caspase 3 expression. Collectively, P. glabra is a rich reservoir of various phytochemicals reflecting a promising potential for alleviation of gastric ulcer through the mediation of inflammatory and apoptotic cascades.
Collapse
|
27
|
Saqr M, Nouri J, Vartiainen H, Tedre M. Robustness and rich clubs in collaborative learning groups: a learning analytics study using network science. Sci Rep 2020; 10:14445. [PMID: 32879398 PMCID: PMC7468117 DOI: 10.1038/s41598-020-71483-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 08/17/2020] [Indexed: 11/10/2022] Open
Abstract
Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group's robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.
Collapse
Affiliation(s)
- Mohammed Saqr
- School of Computing, University of Eastern Finland, Joensuu, Yliopistokatu 2, 80100, Joensuu, Finland.
- Stockholm University - Department of Computer and System Sciences (DSV), Borgarfjordsgatan 12, Kista, PO Box 7003, 164 07, Stockholm, Sweden.
| | - Jalal Nouri
- Stockholm University - Department of Computer and System Sciences (DSV), Borgarfjordsgatan 12, Kista, PO Box 7003, 164 07, Stockholm, Sweden
| | - Henriikka Vartiainen
- University of Eastern Finland, School of Applied Educational Science and Teacher Education, Joensuu, Yliopistokatu 2, 80100, Joensuu, Finland
| | - Matti Tedre
- School of Computing, University of Eastern Finland, Joensuu, Yliopistokatu 2, 80100, Joensuu, Finland
| |
Collapse
|
28
|
Salavaty A, Ramialison M, Currie PD. Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks. PATTERNS (NEW YORK, N.Y.) 2020; 1:100052. [PMID: 33205118 PMCID: PMC7660386 DOI: 10.1016/j.patter.2020.100052] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/17/2020] [Accepted: 05/26/2020] [Indexed: 12/28/2022]
Abstract
Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes.
Collapse
Affiliation(s)
- Adrian Salavaty
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
- Systems Biology Institute Australia, Monash University, Clayton, VIC 3800, Australia
| | - Mirana Ramialison
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
- Systems Biology Institute Australia, Monash University, Clayton, VIC 3800, Australia
| | - Peter D. Currie
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
- EMBL Australia, Monash University, Clayton, VIC 3800, Australia
| |
Collapse
|
29
|
Randhawa V, Pathania S. Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge. Brief Funct Genomics 2020; 19:364-376. [PMID: 32678894 DOI: 10.1093/bfgp/elaa015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/31/2020] [Accepted: 06/15/2020] [Indexed: 01/17/2023] Open
Abstract
Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.
Collapse
Affiliation(s)
- Vinay Randhawa
- Department of Biochemistry, Panjab University, Chandigarh, 160014, India
| | - Shivalika Pathania
- Department of Biotechnology, Panjab University, Chandigarh, 160014, India
| |
Collapse
|
30
|
The Dynamics of Respiratory Microbiota during Mechanical Ventilation in Patients with Pneumonia. J Clin Med 2020; 9:jcm9030638. [PMID: 32120914 PMCID: PMC7141134 DOI: 10.3390/jcm9030638] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 02/21/2020] [Accepted: 02/21/2020] [Indexed: 11/17/2022] Open
Abstract
Bacterial pneumonia is a major cause of mechanical ventilation in intensive care units. We hypothesized that the presence of particular microbiota in endotracheal tube aspirates during the course of intubation was associated with clinical outcomes such as extubation failure or 28-day mortality. Sixty mechanically ventilated ICU (intensive care unit) patients (41 patients with pneumonia and 19 patients without pneumonia) were included, and tracheal aspirates were obtained on days 1, 3, and 7. Gene sequencing of 16S rRNA was used to measure the composition of the respiratory microbiome. A total of 216 endotracheal aspirates were obtained from 60 patients. A total of 22 patients were successfully extubatedwithin3 weeks, and 12 patients died within 28days. Microbiota profiles differed significantly between the pneumonia group and the non-pneumonia group (Adonis, p < 0.01). While α diversity (Shannon index) significantly decreased between day 1 and day 7 in the successful extubation group, it did not decrease in the failed extubation group among intubated patients with pneumonia. There was a significant difference in the change of βdiversity between the successful extubation group and the failed extubation group for Bray-Curtis distances (p < 0.001). At the genus level, Rothia, Streptococcus, and Prevotella correlated with the change of β diversity. A low relative abundance of Streptococci at the time of intubation was strongly associated with 28-day mortality. The dynamics of respiratory microbiome were associated with clinical outcomes such as extubation failure and mortality. Further large prospective studies are needed to test the predictive value of endotracheal aspirates in intubated patients.
Collapse
|
31
|
Ramos PIP, Arge LWP, Lima NCB, Fukutani KF, de Queiroz ATL. Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets. Front Genet 2019; 10:1120. [PMID: 31798629 PMCID: PMC6863976 DOI: 10.3389/fgene.2019.01120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 10/16/2019] [Indexed: 11/13/2022] Open
Abstract
Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.
Collapse
Affiliation(s)
- Pablo Ivan Pereira Ramos
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Luis Willian Pacheco Arge
- Laboratório de Genética Molecular e Biotecnologia Vegetal, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Kiyoshi F. Fukutani
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Fundação José Silveira, Salvador, Brazil
| | - Artur Trancoso L. de Queiroz
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| |
Collapse
|
32
|
Fraunberger E, Esser MJ. Neuro-Inflammation in Pediatric Traumatic Brain Injury-from Mechanisms to Inflammatory Networks. Brain Sci 2019; 9:E319. [PMID: 31717597 PMCID: PMC6895990 DOI: 10.3390/brainsci9110319] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/06/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
Compared to traumatic brain injury (TBI) in the adult population, pediatric TBI has received less research attention, despite its potential long-term impact on the lives of many children around the world. After numerous clinical trials and preclinical research studies examining various secondary mechanisms of injury, no definitive treatment has been found for pediatric TBIs of any severity. With the advent of high-throughput and high-resolution molecular biology and imaging techniques, inflammation has become an appealing target, due to its mixed effects on outcome, depending on the time point examined. In this review, we outline key mechanisms of inflammation, the contribution and interactions of the peripheral and CNS-based immune cells, and highlight knowledge gaps pertaining to inflammation in pediatric TBI. We also introduce the application of network analysis to leverage growing multivariate and non-linear inflammation data sets with the goal to gain a more comprehensive view of inflammation and develop prognostic and treatment tools in pediatric TBI.
Collapse
Affiliation(s)
- Erik Fraunberger
- Alberta Children’s Hospital Research Institute, Calgary, AB T3B 6A8, Canada;
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Michael J. Esser
- Alberta Children’s Hospital Research Institute, Calgary, AB T3B 6A8, Canada;
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Pediatrics, Cumming School Of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| |
Collapse
|
33
|
Yin D, Chen X, Zeljic K, Zhan Y, Shen X, Yan G, Wang Z. A graph representation of functional diversity of brain regions. Brain Behav 2019; 9:e01358. [PMID: 31350830 PMCID: PMC6749480 DOI: 10.1002/brb3.1358] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 05/13/2019] [Accepted: 06/24/2019] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Modern network science techniques are popularly used to characterize the functional organization of the brain. A major challenge in network neuroscience is to understand how functional characteristics and topological architecture are related in the brain. Previous task-based functional neuroimaging studies have uncovered a core set of brain regions (e.g., frontal and parietal) supporting diverse cognitive tasks. However, the graph representation of functional diversity of brain regions remains to be understood. METHODS Here, we present a novel graph measure, the neighbor dispersion index, to test the hypothesis that the functional diversity of a brain region is embodied by the topological dissimilarity of its immediate neighbors in the large-scale functional brain network. RESULTS We consistently identified in two independent and publicly accessible resting-state functional magnetic resonance imaging datasets that brain regions in the frontoparietal and salience networks showed higher neighbor dispersion index, whereas those in the visual, auditory, and sensorimotor networks showed lower neighbor dispersion index. Moreover, we observed that human fluid intelligence was associated with the neighbor dispersion index of dorsolateral prefrontal cortex, while no such association for the other metrics commonly used for characterizing network hubs was noticed even with an uncorrected p < .05. CONCLUSIONS This newly developed graph theoretical method offers fresh insight into the topological organization of functional brain networks and also sheds light on individual differences in human intelligence.
Collapse
Affiliation(s)
- Dazhi Yin
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Xiaoyu Chen
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Kristina Zeljic
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Yafeng Zhan
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xiangyu Shen
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Gang Yan
- School of Physics Science and EngineeringTongji UniversityShanghaiChina
| | - Zheng Wang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
- Kunming Institute of ZoologyChinese Academy of SciencesKunmingChina
| |
Collapse
|
34
|
Lu H, Li F, Sánchez BJ, Zhu Z, Li G, Domenzain I, Marcišauskas S, Anton PM, Lappa D, Lieven C, Beber ME, Sonnenschein N, Kerkhoven EJ, Nielsen J. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun 2019; 10:3586. [PMID: 31395883 PMCID: PMC6687777 DOI: 10.1038/s41467-019-11581-3] [Citation(s) in RCA: 148] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 07/17/2019] [Indexed: 01/06/2023] Open
Abstract
Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae--an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.
Collapse
Affiliation(s)
- Hongzhong Lu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Benjamín J Sánchez
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Zhengming Zhu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
- School of Biotechnology, Jiangnan University, 1800 Lihu Road, 214122, Wuxi, Jiangsu, China
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Simonas Marcišauskas
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Petre Mihail Anton
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Dimitra Lappa
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Christian Lieven
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Moritz Emanuel Beber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark.
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
| |
Collapse
|
35
|
Oldham S, Fulcher B, Parkes L, Arnatkevic̆iūtė A, Suo C, Fornito A. Consistency and differences between centrality measures across distinct classes of networks. PLoS One 2019; 14:e0220061. [PMID: 31348798 PMCID: PMC6660088 DOI: 10.1371/journal.pone.0220061] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 07/08/2019] [Indexed: 11/20/2022] Open
Abstract
The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.
Collapse
Affiliation(s)
- Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- * E-mail:
| | - Ben Fulcher
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- School of Physics, The University of Sydney, Sydney, New South Wales, Australia
| | - Linden Parkes
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Aurina Arnatkevic̆iūtė
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Chao Suo
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| |
Collapse
|
36
|
Gazouli M, Dovrolis N, Franke A, Spyrou GM, Sechi LA, Kolios G. Differential genetic and functional background in inflammatory bowel disease phenotypes of a Greek population: a systems bioinformatics approach. Gut Pathog 2019; 11:31. [PMID: 31249629 PMCID: PMC6570833 DOI: 10.1186/s13099-019-0312-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 05/30/2019] [Indexed: 12/13/2022] Open
Abstract
Background Crohn’s disease (CD) and Ulcerative colitis (UC) are the two main entities of inflammatory bowel disease (IBD). Previous works have identified more than 200 risk factors (including loci and signaling pathways) in populations of predominantly European ancestry. Our study was conducted on an extended population-specific cohort of 573 Greek IBD patients (364 CD and 209 UC) and 445 controls. Aims To highlight the different genetic and functional background of IBD and its phenotypes, utilizing contemporary systems bioinformatics methodologies. Methods Disease-associated SNPs, obtained via our own 89 loci IBD risk GWAS panel, were detected with the whole genome association analysis toolset PLINK. These SNPs were used as input for 2 novel and different pathway analysis methods to detect functional interactions. Specifically, PathwayConnector was used to create complementary networks of interacting pathways whereas; the online database of protein interactions STRING provided protein–protein association networks and their derived pathways. Network analyses metrics were employed to identify proteins with high significance and subsequently to rank the signaling pathways those participate in. Results The reported complementary pathway and enriched protein–protein association networks reveal several novel and well-known key players, in the functional background of IBD like Toll-like receptor, TNF, Jak-STAT, PI3K-Akt, T cell receptor, Apoptosis, MAPK and B cell receptor signaling pathways. IBD subphenotypes are found to have distinct genetic and functional profiles which can contribute to their accurate identification and classification. As a secondary result we identify an extended network of diseases with common molecular background to IBD. Conclusions IBD’s burden on the quality of life of patients and intricate functional background presents us constantly with new challenges. Our data and methodology provide researchers with new insights to a specific population, but also, to possible differentiation markers of disease classification and progression. This work, not only provides new insights into the interplay among IBD risk variants and their related signaling pathways, elucidates the mechanisms underlying IBD and its clinical sequelae, but also, introduces a generalized bioinformatics-based methodology which can be applied to studies of different disorders. Electronic supplementary material The online version of this article (10.1186/s13099-019-0312-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Maria Gazouli
- 1Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, Michalakopoulou 176, 11527 Athens, Greece
| | - Nikolas Dovrolis
- 2Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Xanthi, Greece
| | - Andre Franke
- 3Institute of Clinical Molecular Biology, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - George M Spyrou
- 4Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Leonardo A Sechi
- 5Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - George Kolios
- 2Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Xanthi, Greece
| |
Collapse
|
37
|
Pournoor E, Elmi N, Masoudi-Nejad A. CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks. Curr Genomics 2019; 20:69-75. [PMID: 31015793 PMCID: PMC6446483 DOI: 10.2174/1389202919666181213101540] [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: 08/11/2018] [Revised: 10/19/2018] [Accepted: 12/02/2018] [Indexed: 12/17/2022] Open
Abstract
Background Complexity and dynamicity of biological events is a reason to use comprehen-sive and holistic approaches to deal with their difficulty. Currently with advances in omics data genera-tion, network-based approaches are used frequently in different areas of computational biology and bio-informatics to solve problems in a systematic way. Also, there are many applications and tools for net-work data analysis and manipulation which their goal is to facilitate the way of improving our under-standings of inter/intra cellular interactions. Methods In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.
Collapse
Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Naser Elmi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| |
Collapse
|
38
|
Oldham S, Fornito A. The development of brain network hubs. Dev Cogn Neurosci 2019; 36:100607. [PMID: 30579789 PMCID: PMC6969262 DOI: 10.1016/j.dcn.2018.12.005] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/24/2018] [Accepted: 12/11/2018] [Indexed: 01/31/2023] Open
Abstract
Some brain regions have a central role in supporting integrated brain function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and to consider how they shape the function of the maturing brain. Here, we review evidence examining how brain network hubs, both in structural and functional connectivity networks, develop over the prenatal, neonate, childhood, and adolescent periods. The available evidence suggests that structural hubs of the brain arise in the prenatal period and show a consistent spatial topography through development, but undergo a protracted period of consolidation that extends into late adolescence. In contrast, the hubs of brain functional networks show a more variable topography, being predominantly located in primary cortical areas in early development, before moving to association areas by late childhood. These findings suggest that while the basic anatomical infrastructure of hubs may be established early, the functional viability and integrative capacity of these areas undergoes extensive postnatal maturation. Not all findings are consistent with this view however. We consider methodological factors that might drive these inconsistencies, and which should be addressed to promote a more rigorous investigation of brain network development.
Collapse
Affiliation(s)
- Stuart Oldham
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia.
| | - Alex Fornito
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia
| |
Collapse
|
39
|
Lázaro-Guevara J, Flores-Robles B, Garrido K, Pinillos-Aransay V, Elena-Ibáñez A, Merino-Meléndez L, López-Martínez J, Victoriano-Lacalle R. Gene's hubs in retinal diseases: A retinal disease network. Heliyon 2018; 4:e00867. [PMID: 30417144 PMCID: PMC6218668 DOI: 10.1016/j.heliyon.2018.e00867] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 06/28/2018] [Accepted: 10/11/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Retinal diseases associated with the dysfunction or death of photoreceptors are a major cause of blindness around the world, improvements in genetics tools, like next generation sequencing (NGS) allows the discovery of genes and genetic changes that lead to many of those retinal diseases. Though, there very few databases that explores a wide spectrum of retinal diseases, phenotypes, genes, and proteins, thus creating the need for a more comprehensive database, that groups all these parameters. METHODS Multiple open access databases were compiled into a new comprehensive database. A biological network was then crated, and organized using Cytoscape. The network was scrutinized for presence of hubs, measuring the concentration of grouped nodes. Finally, a trace back analysis was performed in areas were the power law reports a high r-squared value near one, that indicates high nodes density. RESULTS This work leads to creation of a retinal database that includes 324 diseases, 803 genes, 463 phenotypes, and 2461 proteins. Four biological networks (1) a disease and gene network connected by common phenotypes, (2) a disease and phenotype network connected by common genes, (3) a disease and gene network with shared disease or gene as the cause of an edge, and (4) a protein and disease network. The resulting networks will allow users to have easier searching for retinal diseases, phenotypes, genes, and proteins and their interrelationships. CONCLUSIONS These networks have a broader range of information than previously available ones, helping clinicians in the comprehension of this complex group of diseases.
Collapse
Affiliation(s)
| | | | - K. Garrido
- Paediatrics Department Guatemalan Social Secure Guatemala, Guatemala
| | | | | | | | | | | |
Collapse
|
40
|
A systematic survey of centrality measures for protein-protein interaction networks. BMC SYSTEMS BIOLOGY 2018; 12:80. [PMID: 30064421 PMCID: PMC6069823 DOI: 10.1186/s12918-018-0598-2] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 06/22/2018] [Indexed: 12/12/2022]
Abstract
Background Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. Results We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. Conclusions The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node. Electronic supplementary material The online version of this article (10.1186/s12918-018-0598-2) contains supplementary material, which is available to authorized users.
Collapse
|
41
|
Jalili M, Gebhardt T, Wolkenhauer O, Salehzadeh-Yazdi A. Unveiling network-based functional features through integration of gene expression into protein networks. Biochim Biophys Acta Mol Basis Dis 2018; 1864:2349-2359. [PMID: 29466699 DOI: 10.1016/j.bbadis.2018.02.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/31/2018] [Accepted: 02/13/2018] [Indexed: 02/02/2023]
Abstract
Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.
Collapse
Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran; Hematologic Malignancies Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
| |
Collapse
|