Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Nicotiana tabacum—TEV Pathosystem and Transcriptomic Data
2.2. Identification of Genes with Biphasic Gene Expression Pattern along Disease Categories
2.3. Criteria for Identification and Statistical Evaluation of DNB
2.4. Functional Enrichment Analysis of DNBs
2.5. Network Analyses
3. Results and Discussion
3.1. Distribution and Characterization of Genes Showing Biphasic Expression Profiles across Disease Categories
3.1.1. Characterization of Early Biphasic Genes
3.1.2. Characterization of Intermediate Biphasic Genes
3.1.3. Characterization of Late Biphasic Genes
3.2. Mapping Biphasic Genes into A. thaliana AI-1 PPIN
Inference of PPIN-Based DNBs
3.3. Mapping Biphasic Genes into A. thaliana TRN
Inference of TRN-Based DNBs
3.4. Topological Properties of the DNBs Subnetworks
3.5. Mutations in Different Viral Proteins and Their Effect on the Likelihood of Disease Progression
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Network Property | PPIN | PPINDNB | TRN | TRNDNB 1 |
Shortest path | 3.529 ± 0.739 | 3.650 ± 0.990 * | 3.476 ± 0.274 | 3.355 ± 0.216 * |
Betweenness centrality | 0.023 ± 0.388 | 0.015 ± 0.104 | 1.144 ± 3.739 × 10−4 | 2.612 ± 7.300 × 10−4 |
Closeness centrality | 0.292 ± 0.094 | 0.308 ± 0.158 | 0.289 ± 0.022 | 0.299 ± 0.019 * |
Clustering coefficient | 0.138 ± 0.198 | 0.152 ± 0.242 | 0.213 ± 0.137 | 0.184 ± 0.107 * |
Degree | 20.138 ± 31.148 | 20.523 ± 41.789 | 58.905 ± 60.034 | 90.513 ± 85.910 * |
Eccentricity | 7.544 ± 1.300 | 7.530 ± 1.859 | 120.526 ± 82.141 | 5.930 ± 0.443 * |
Neighborhood connectivity | 65.840 ± 73.970 | 38.605 ± 45.927 * | 58.898 ± 60.027 | 98.598 ± 53.418 * |
Topological coefficient | 0.161 ± 0.178 | 0.195 ± 0.222 | 0.096 ± 0.083 | 0.065 ± 0.050 * |
Critical exponent degree distribution 2 | −2.749 ± 0.399 | −2.043 ± 0.366 * | −3.225 ± 0.174 | −2.512 ± 0.210 * |
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Tarazona, A.; Forment, J.; Elena, S.F. Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers. Viruses 2020, 12, 16. https://doi.org/10.3390/v12010016
Tarazona A, Forment J, Elena SF. Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers. Viruses. 2020; 12(1):16. https://doi.org/10.3390/v12010016
Chicago/Turabian StyleTarazona, Adrián, Javier Forment, and Santiago F. Elena. 2020. "Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers" Viruses 12, no. 1: 16. https://doi.org/10.3390/v12010016
APA StyleTarazona, A., Forment, J., & Elena, S. F. (2020). Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers. Viruses, 12(1), 16. https://doi.org/10.3390/v12010016