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).

Mortality in hemodialysis: Synchrony of biomarker variability indicates a critical transition

“…We propose that beyond specific diseases [17][18][19] , the broad health status of an individual can be assessed using critical transition theory, with general outcomes such as death often reflecting a critical transition, which we refer to as a "physiological collapse" of the organism. Successful prediction of impending physiological collapse could allow timely interventions to reduce mortality and/or start end-of-life planning.…”

Excessive supply of glucose elicits an NF‐κB2‐dependent glycolysis in lactating goat mammary glands

“…Primary knowledge on mammary metabolic networks is based on downstream pathways related to milk synthesis (protein, lactose, and fat, 12), leading to limited conclusions of only some of the functional molecules involved. The recently developed dynamical network biomarker (DNB) theory described groups of genes that enter into resonance as the organ gets closer to the irreversible transition from one state to another, and helped to seek for early warnings of state transition 13 . Thus, a DNB‐based strategy should be developed to detect the metabolic disorders by analyzing the transcriptome and reveal key pathways and molecules involved in the metabolic disorders.…”

“…The recently developed dynamical network biomarker (DNB) theory described groups of genes that enter into resonance as the organ gets closer to the irreversible transition from one state to another, and helped to seek for early warnings of state transition. 13 Thus, a DNBbased strategy should be developed to detect the metabolic disorders by analyzing the transcriptome and reveal key pathways and molecules involved in the metabolic disorders.…”

Special Issue: “The Complexity of the Potyviral Interaction Network”

“…In an additional four articles, the molecular responses of crops to potyvirus infection are analyzed through high-throughput transcriptomics approaches. Different pathosystems that yield different outcomes in potyviral infection are analyzed, which include antagonism or synergism induced in papaya by mixed-infection of papaya ringspot potyvirus and papaya mosaic potexvirus [ 6 ], tomato recovery from potato virus Y (PVY) infection [ 7 ], potato susceptibility or resistance to PVY [ 8 ], soybean susceptibility to soybean mosaic virus depending on light conditions [ 9 ], and transition from mild to severe tobacco etch disease in tobacco [ 10 ]. In particular, these studies highlight how the outcome of an infection can be linked to the regulation of genes involved in plant immunity and the role played by small RNAs in those processes.…”

“…In particular, these studies highlight how the outcome of an infection can be linked to the regulation of genes involved in plant immunity and the role played by small RNAs in those processes. In their original paper, Tarazona et al [ 10 ] applied Dynamical Network Biomarker (DNB) theory for the first time to a plant virus to identify genes or network elements that could be used as early-warnings of the transition from health to disease. Through physiological and ultrastructural analyses, Otulak-Koziel et al [ 11 ] highlighted the role of plant expansins and extensins in the dynamics of the cell wall structure during incompatible interactions in resistant and compatible interactions in susceptible potato upon potato virus Y infections.…”