Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers

Abstract

Complex systems exhibit critical thresholds at which they transition among alternative phases. Complex systems theory has been applied to analyze disease progression, distinguishing three stages along progression: (i) a normal noninfected state; (ii) a predisease state, in which the host is infected and responds and therapeutic interventions could still be effective; and (iii) an irreversible state, where the system is seriously threatened. The dynamical network biomarker (DNB) theory sought for early warnings of the transition from health to disease. Such DNBs might range from individual genes to complex structures in transcriptional regulatory or protein-protein interaction networks. Here, we revisit transcriptomic data obtained during infection of tobacco plants with tobacco etch potyvirus to identify DNBs signaling the transition from mild/reversible to severe/irreversible disease. We identified genes showing a sudden transition in expression along disease categories. Some of these genes cluster in modules that show the properties of DNBs. These modules contain both genes known to be involved in response to pathogens (e.g., ADH2, CYP19, ERF1, KAB1, LAP1, MBF1C, MYB58, PR1, or TPS5) and other genes not previously related to biotic stress responses (e.g., ABCI6, BBX21, NAP1, OSM34, or ZPN1).

Keywords: DNB; Potyvirus; Tobacco etch virus; complex systems; phase transitions; plant-virus interaction; protein-protein interaction networks; response to infection; systems biology.

Figure 1

Schematic illustration of the dynamical features of disease progression from a health to a disease state through a predisease state. (a–d) Representation of the evolution of the dynamical system between the stable equilibria represented by the healthy (b) and disease (d) states via an unstable predisease state (c). (e–g) Represent a molecular network where the correlations and deviations of different molecules (z_i) are described by the thickness of edges and the color of nodes, respectively. When the system approaches the predisease state, deviations increase drastically, and the correlation among some molecules increases, whereas their correlations with other elements in the network decrease. Molecules z₁, z₂, z₃ represent the dynamical network biomarker (DNB). (h) Examples of the dynamical fluctuations in the concentration of the molecules in the DNB at the pre-disease state. Reproduced under a creative commons license from [10].

Figure 2

Relationship between the relative fitness of each tobacco etch potyvirus (TEV) genotype (ordinate), the severity of symptoms they induce in N. tabacum plants (numbers inside the bars, semiquantitative scale ranging from 1 to 5), and the similarity in the transcriptomic profile of infected plants (dendrogram in top). Error bars represent ±1 SEM.

Figure 3

Number of significant sudden phase transitions observed in the levels of gene expression (log₂-fold change) along the seven disease categories defined in Section 2.1.

Figure 4

Mapping of all genes showing a biphasic pattern (in yellow) in their expression into the A. thaliana AI-1 protein–protein interaction networks (PPIN).

Figure 5

Evolution of the I* index along disease categories. (A) For each of the 121 candidate PPIN-based DNBs. (B) For each of the 11 candidate transcriptional regulatory network (TRN)-based DNBs. In both cases the largest number of peaks in I* corresponds to the disease category IV.

Figure 6

Mapping of genes belonging to (A) ^TRNDNB-36 and (B) ^TRNDNB-40 (in yellow) into the A. thaliana TRN model [40].