Viral Spread Model

Plant Structure

-Plasmodesmata -Phloems/Vascular System

Virus

-Initial Infection Sites -Founder Population -Viral Spread Rates -Viral Spread Chance -Viral Assembly

Plant Response

-SA Chance (Plant Defense) -Lysis Chance -SAR -SAR Spread Chance

Biological Background

Viral Spread Mechanisms

Intercellular

Cauliflower mosaic virus exploits host transport mechanisms in order to spread. Neighbouring plant cells share cytoplasmic connections through connections called plasmodesmata. These symplastic connections are exploited by CaMV to spread to neighbouring cells, but it is a fairly slow process. The virions must travel between the plasma membrane and the desmotubule (cytoplasmic channel) of the plasmodesmata, a very narrow passage. The size of molecule able to diffuse through the plasmodesmata is determined by the size exclusion limit (SEL), which is affected by the gating properties of the plasmodesmata. In another slow cell-to-cell process, the virus can move through tubules formed through the cell wall. However, CaMV is able to spread to different regions of the plant quite quickly using the plant’s phloem (sugar-transport network). (Carrington et al. 1996) Microtubules are a key component of intercellular transmission as well as intracellular motion. They form viral inclusions that encourage uptake by aphids (the major plant-to-plant transport agent) and of viral factories (Niehl et al., 2013). CaMV can induce the formation of microtubules in order to increase its rate of spread (Carrington et al. 1996).

Systemic Infection

-Phloem/Vascular System

Plant Defense Mechanisms

Hypersensitive Response (HR)

Interplant signalling plays an important role in the hypersensitive response of plants, where plant cells selectively undergo apoptosis in order to destroy infected or damaged cells, such as in the event of pathogen attacks like CaMV. A main intercellular signaling molecule is hydrogen peroxide (H2O2), formed during electron transport processes (Neill et al, 2002). This is produced in elevated levels the event of pathogen attack and other stresses, and can damage DNA and proteins (Neill et al, 2002). It allows for the localization of apoptosis which occurs as a result of the hypersensitive response as well as increased expression of defense genes, which it modulates during defense response (Neill et al, 2002). H2O2 has been observed in tobacco plants to induce the production of proteasomes linked to the degradation of cells in programmed cell death (Neill et al, 2002). In Arabidopsis thaliana, it has been observed that increased generation of H2O2 leads to an increase of calcium ions in the form of cytosolic calcium, which, triggering a cascade of reactions leading to the apoptosis of infected cells (Neill et al, 2002). H2O2 has also been observed inducing the expression of glutathione S-transferase (GST) and phenylalanine ammonia‐lyase (PAL), both of which are defense-related genes, as well as genes involved in the production and degradation of H2O2 (Neill et al, 2002). Additionally, it has been observed to cause the stomatal closure of cells (Neill et al, 2002). Another intercellular signaling molecule is nitric oxide (NO), found primarily in its gaseous form, which may be produced at the same time as (H2O2) after pathogen challenge, and induces a similar defense response as (H2O2). (Neill et al, 2002) It increases the gene expression of defensive genes such as PAL1 and GST. (Neill et al, 2002) Additionally, NO may have a role in iron-level regulation in plants, and redox signaling through its potential involvement with pathogen-induced oxygenase. (Neill et al, 2002) There is limited research for NO as a plant signal, however, it has been researched extensively as a signaling molecule in mammalian cells (Neill et al, 2002). Like in mammalian cells, NO has been observed increasing levels of cyclic GMP in the event of pathogen challenge and inducing programmed cell death. (Neill et al, 2002) In additional to working concurrently with each other, NO and H2O2 also interact with a whole host of other signaling molecules, such as jasmonic acid, ethylene, and salicylic acid (Neill et al, 2002), which have to be taken into consideration when studying intercellular plant signaling as a whole.

Systemic Acquired Resistance (SAR)

Mechanisms

The systemic acquired resistance (SAR) defense mechanism, or immunization of plants, is a broad, long-term increased resistant to future infections. (Ryals et al., 1994) This is similar to the increased resistance against diseases in mammals, after having been infected. (Ryals et al., 1994) It is important to note that this resistance is not triggered by mechanical damage caused by factors such as herbivore attack. (Ryals et al., 1994) This mechanism is only activated after a pathogen is detected within the plant, triggering defenses through signals (Ryals et al., 1994) Salicylic acid (SA) has been identified as a molecule with an indispensable role in the pathway to systemic acquired resistance. (Klessig et al., 2000) Additionally, NO also has an important role in activating systemic acquired resistance. (Klessig et al., 2000) During the initial wave of defense after pathogen detection, there is an “oxidative burst” wherein levels of oxidative species suddenly increase. (Klessig et al., 2000) This is accompanied by cell wall protein linkage, the activation of kinase and increased gene expression defensive genes. (Klessig et al., 2000) A second wave of defense is found in the hypersensitive response, wherein lesions form (from programmed cell death). (Klessig et al., 2000) NO and SA play an interconnected role with each other, where reactive oxygen species (ROS) such as NO have been observed accumulating SA, and in turn, SA triggers ROS production (including NO and H2O2). (Klessig et al., 2000)

Signalling and Spread

Software Choices

Originally, three different agent-based modelling (ABM) software packages (MASON, MESA and Netlogo) were chosen out of several as the top candidates to create the viral spread simulation. All of the languages required for the simulation packages were unfamiliar to the team, and thus would have to be learned from scratch. Netlogo was chosen to create the final simulation due to a variety of its strengths as well as time constraints. Although Netlogo was the most unfamiliar-looking language, it presented itself as the simulation package with the smallest learning curve. Additionally, it had readily-available documentation and a built-in GUI. MESA was a Python-based simulation package and ultimately was not chosen due to its documentation not being as detailed or as readily available as MASON or Netlogo’s, as well as the level of familiarity with Python required to create the model being too high to achieve within the time constraints. MASON, a Java-based simulation package, despite having excellent, easily-found documentation did not have a built-in GUI, and did not have as many built in-functions as the Netlogo, thus requiring more work to get off the ground. Although in some cases the fewer amount of built-in functions could have proved to be an advantage (it would have added more flexibility and customization to the simulation), due to the time constraints and the demand of a level familiarity with Java (as with MESA) to create a simulation with MASON, Netlogo was thus chosen over MASON.