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| − | <h1>Viral Spread</h1> | + | {{Waterloo}} |
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| | + | <h1>Viral Spread Model</h1> |
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| − | <h2>Viral Spread Within Arabidopsis</h2> | + | <h2>Plant Structure</h2> |
| − | | + | -Plasmodesmata |
| − | <h3>Introduction</h3>
| + | -Phloems/Vascular System |
| − | <p>To be written...</p> | + | <h2>Virus</h2> |
| − | | + | -Initial Infection Sites |
| − | <h3>Plant Signalling and Defenses<h3>
| + | -Founder Population |
| − | | + | -Viral Spread Rates |
| − | <h4>Overview</h4>
| + | -Viral Spread Chance |
| − | | + | -Viral Assembly |
| − | <p>As a result of the vulnerabilities of plants towards various attacks by pathogens and herbivores, as well as environmental damage, plants have developed several lines of defense in order to mitigate damage. These include the first response towards pathogens, basal resistance, where microbe-associated molecular patterns (MAMP) are recognized and defense mechanisms are triggered. (Klessig et al., 2000) Later waves of defense include RNA silencing, as well as the hypersensitive response (HR) (Klessig et al., 2000). Even after the initial pathogen threat is over, plants are able to develop a long-term state of systematic resistance, similar to being immunized against the specific pathogen. (Klessig et al., 2000) In addition to these defenses, plants such as the Arabidopsis thaliana have developed complex systems for defense signaling. These include the intercellular interactions of different chemical signals to target defense mechanisms to a certain area of the plant, (Klessig et al., 2000) as well as interplant signaling, so that when a nearby plant is attacked, neighbouring plants are alerted to this, and as a result increase production of defense-related molecules (Song et al., 2010). </p>
| + | <h2>Plant Response</h2> |
| − | | + | -SA Chance (Plant Defense) |
| − | <h4>RNA Silencing</h4>
| + | -Lysis Chance |
| − | <p>For a more detailed description of RNA silencing please see the Viral Assembly page. Link here: </p>
| + | -SAR |
| − | | + | -SAR Spread Chance |
| − | <h4>Hypersensitive Response (HR)</h4> | + | <h1>Biological Background</h1> |
| − | | + | <h2>Viral Spread Mechanisms</h2> |
| − | <p>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.
| + | -Plasmodesmata |
| − | | + | -Phloem/Vascular System |
| − | 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).
| + | <h2>Plant Defense Mechanisms</h2> |
| − | | + | -HR Apoptosis |
| − | 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)
| + | -SAR |
| − | | + | -Mechanisms |
| − | 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. </p>
| + | -Signalling and Spread |
| − | | + | <h1>Software Choice</h1> |
| − | <h4>Systemic Acquired Resistance (SAR)</h4>
| + | <p> 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. </p> |
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| − | <p>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)</p> | + | </div> |
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| − | <h4>Signalling - Interplant</h4>
| + | {{Waterloo_Footer}} |
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| − | <p>As a response to attacks by pathogens, plants can signal this to other plants through chemical emissions in the air. Additionally, plants may also potentially be able to communicate through common mycorrhizal networks (CMN) created by mycorrhizal fungi, connecting the roots of different plants together (Song et al., 2010). Mycorrhizal fungi found in the soil have a symbiotic relationship between them and the roots and the roots of the plants, as well as giving additional defense to the plant itself (Song et al., 2010). They allow nutrients, carbon and water to travel from plant to plant (Song et al., 2010). Song et al studied the potential of the CMN carry plant communication signals in tomato plants. The reception of these signals from infected plants is highly advantageous to non-infected, neighbouring plants, as it allows the non-infected plant to increase their defenses, including increased levels of defends enzymes, and the expression of genes related to their defenses (Song et al., 2010). In Song et al, they measured the levels of six defense enzymes in tomato plants: peroxidase (POD), polyphenol oxidase (PPO), chitinase, β-1,3-glucanase, phenylalanine ammonia-lyase (PAL) and lipoxygenase (LOX). The levels of all of the of the measured defense enzymes as well as gene expression encoding for these enzymes increased in CMN connected plants in the presence of a A.solani pathogen challenge (Song et al., 2010). It was proposed in the study that the speed of intercellular signals is faster than the transfer of signal molecules in the CMN, and that it gives a greater advantage over air-spread chemical signals due to the different factors such as the unpredictability of the wind and the space between plants. (Song et al., 2010).</p>
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| − | <h3>Mechanisms of Viral Transport</h3> | + | |
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| − | <h4>Long-Distance CaMV Phloem Transport</h4>
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| − | Summaries of three relevant papers
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| − | <h5>Effects of Host Plant Development and Genetic Determinants on the Long-Distance Movement of Cauliflower Mosaic Virus in Arabidopsis</h5>
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| − | Leisner et al. 1993 in The Plant Cell - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC160262/pdf/050191.pdf
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| − | <h6>Intro</h6> | + | |
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| − | <p>Researchers used plant skeleton hybridization, a whole plant in situ hybridization technique to show that conditions influencing the rate of plant development dramatically impact the long-distance movement of CaMV. Mature leaves provide nutrients for the plant and don’t need to import them. CaMV follows the flow of photoassimilates (nutrients) from source to sink leaves, so if the leaves are already fully developed they become inaccessible to the virus. | + | |
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| − | The connections between phloem parenchyma and the bundle sheath cells (sieve tube elements, companion cells - see https://en.wikipedia.org/wiki/Phloem#/media/File:Phloem_cells.svg for an image of basic phloem structure) are different than the plasmodesmata between other cell types. Previous research showed that mutations in the coat protein or assembly origin of TMV eliminated long-distance transport while cell-to-cell movement was unaffected.
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| − | If a virus successfully invades the vascular system of a susceptible plant, it moves preferentially through prescribed pathways. CaMV is transported along with the flow of photoassimilates and is therefore unable to infect mature leaves. Further, young leaves stop importing viruses before they stop importing nutrients, so the susceptible regions of the plant are reduced throughout the course of development for plants with determinate growth patterns (like Arabidopsis). This paper found that ecotypes of Arabidopsis that flowered quickly appeared resistant to systemic CaMV infection, while plants subjected to suboptimal growth conditions which caused delayed flowering were more susceptible to systemic infection.
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| − | A form of resistance unrelated to developmental constraints was found in an Arabidopsis ecotype known as Enkheim-2 (En-2), which limited viral transport. It appeared to be conferred by a single, dominant trait.</p>
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| − | <h6>Results</h6>
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| − | <p>They extended the leaf skeleton hybridization technique to whole plants, hybridizing a labeled viral DNA probe to prepared and fixed Arabidopsis plants. The primer binds to both the virions and viral DNA released from virions during the preparation of the plant skeleton. There were some issues with the method (couldn’t detect small amounts of virus), but it was able to provide good information about systemically infected leaves. They used the standard Arabidopsis ecotype, Columbia (Col-0) and CaMV isolate CM4-184 to establish the validity of their technique. They found vial DNA in the plant’s roots, rosette leaves, flower stalks, and cauline leaves in young plants and in seed pods (siliques) in older plants.
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| − | They didn’t determine which specific gene conferred resistance in the En-2, but by crossing it with their wild type (Col-0), they determined it is likely a single gene acting in a dominant pattern.</p>
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| − | <h5>Cell-to-Cell and Long Distance Transport of Viruses in Plants</h5>
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| − | Carrington, Kasschau, Mahajan, Schaad; The Plant Cell; 1996 - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC161306/pdf/081669.pdf
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| − | <h6>Cell-to-Cell</h6>
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| − | <p>It should be emphasized that besides its role in tubule formation, the CaMV MP displays ssRNA binding activity and limited sequence similarity with the TMV MP (Citovsky et al., 1991; Koonin et al., 1991; Thomas and Maule, 1995b), findings that could reflect multiple modes of CaMV transport.</p>
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| − | <h6>Long Distance</h6>
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| − | <p>The entrance into and exit from sieve tube elements are critical points along transport pathway. The plasmodesmata which connects a sieve tube element to its companion cell is special. There is extensive branching near the companion cell, no ER near the pore on the sieve element side (though it may be present over the pore inside the sieve element), and different gating capacities.</p>
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| − | <h5>Phloem loading and unloading of Cowpea mosaic virus in Vigna unguiculata</h5>
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| − | Silva et al; Journal of General Virology; 2002 - http://jgv.sgmjournals.org/content/journal/jgv/10.1099/0022-1317-83-6-1493?crawler=true&mimetype=application/pdf#F3
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| − | <h6>Intro</h6>
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| − | <p>Viruses spread from infected epidermal cells through the underlying mesophyll cells to the vascular bundles, where it can then spread quickly to distant regions of the plant through the phloem. It is well-known that plant viruses adapt the host’s plasmodesmata using MP to transport their genome/virions into neighbouring cells, but knowledge of vascular movement is much more limited. There are different viral mechanisms used to move between mesophyll cells and to enter/exit vascular cells.
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| − | Unlike Tobacco mosaic virus, CPMV are transported as mature virions (not in absence of CP) through virus-induced tubules that cross the walls of adjacent cells. CPMV represents a large group of viruses (which includes caulimoviruses) that use tubules to guide the movement of virions, but no information is available on the mechanisms for entry/exit into the vasculature (do tubules guide entry/exit into sieve elements?).
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| − | CPMV viruses were made to express GFP, the gene inserted into the CPMV RNA-2 coding region. These recombinant viruses were used to identify preferred sites for viral loading/unloading. This paper might prove useful to us should we decide to visualise the spread of the virus, but as most of its methods involve tools like diamond knives, it might be best to look elsewhere first.</p>
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| − | <h6>Results</h6>
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| − | <p>All plant tissues were infected when inoculated at an early stage, as when the cowpea’s third trifoliate leaf was still folded. When plants had a second trifoliate leaf but no third leaf, the virus unloaded in the younger, developing parts of the plant. Plants already in possession of a third leaf supported CPMV replication, but no systemic spread was observed.
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| − | In young plants (first trifoliate leaf still folded) the researchers looked at how long it took the virus to spread to different parts of the plant. To determine how long it takes for the virus to enter the vascular system, the researchers would inoculate a leaf and then remove it up to 7 days afterwards. In order for the virus to spread to the rest of the plant, the leaf must have been removed at least two days after the inoculation – by 2 days p.i., CPMV had been loaded into the primary leaf phloem and transported into the stem. Table 1 from http://jgv.sgmjournals.org/content/journal/jgv/10.1099/0022-1317-83-6-1493?crawler=true&mimetype=application/pdf#F3 shows the rate of infection for the cowpea plant. </p>
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| − | <h6>Discussion</h6>
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| − | <p>The researchers concluded that the virus spreads to developing leaves, following the flow of photoassimilates, using the cowpea’s phloem. Cowpea mosaic virus could be loaded into the phloem of both major and minor veins, either at the vein terminus, a gap at a vein branch, or the side of a vein. Minor veins seem to be preferred. CPMV is able to directly enter the phloem stream from surrounding parenchyma tissue. It exits exclusively from major veins (prefentially class III veins) and demonstrates a similar pattern of unloading and accumulation as is used by TMV.</p>
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| − | <h3>Intracelluar</h3>
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| − | <p>The virus is transported as an icosahedral virion containing its packaged dsDNA (Carrington et al. 1996). Typically, the plant viruses that are transmitted using insect vectors use actin for intracellular transport (Niehl et al, 2013). However, microtubules play an important role in the transport of CaMV (Niehl et al., 2013).
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| − | The plant’s endocytic pathway is also utilized by CaMV for transport, using three tyrosine-sorting signals. These signals are integral for the formation of microtubules that the virus induces in the plant in order to further its intracellular and intercellular transport. (Carluccio and Stavolone, 2014)</p>
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| − | <h3>Intercellular</h3>
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| − | <p>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)
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| − | 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).</p>
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| − | <h3>Between Plants</h3>
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| − | <p>Cauliflower Mosaic Virus, like the majority of plant-infecting viruses, is transmitted between host plants by vectors – in this case, aphids (Whitfield et al., Virology, 2015). Almost 30% of all plant viruses currently described are spread by aphids (Brault et al., 2010).</p>
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| − | <h2>Modelling Viral Spread</h2>
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| − | <h3>Overview</h3>
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| − | <p>Most of the existing work on infection spread has focused on the cellular and population levels; the middle ground of within-host spread is relatively unexplored. However, the mechanisms discussed above, of both short- and long-range viral transport discussed above provide a basis for understanding, which has been mathematized by several papers from Santiago Elena’s lab.</p>
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| − | <h3>Time to systemic infection</h3>
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| − | <p>Rodrigo and colleagues (Rodrigo et al. 2014) explicitly consider within-host dynamics as governed by short- and long-term dynamics. In this model, the infection expands outward from the primary infection site, becoming systemic once it reaches the vasculature (and after an additional latency period for vascular movement). The area of local spread required before reaching the vascular system follows a normal distribution, which yields variable times to systemic infection. This model also accounts for multiple sites of initial infection, which operate independently. The crucial virus-dependent parameters influencing time to systemic infection are the two latency periods (for the initial infected cell and for vascular transport) and the diffusion rate for cell-to-cell viral infection. These models were validated by experimental trials with two variants (low and high diffusion constants) of Turnip Mosaic Virus in nicotiana benthamiana. Their parameters cannot directly translate to adabidopsis, but it is a useful framework for predicting variable times to systemic infection.</p>
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| − | <h3>Tracking infection spread in individual leaves</h3>
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| − | <p>Tromas and colleagues (Tromas et al. 2014) take a different theoretical model: individual leaves are considered as each having their own internal dynamics given by a susceptible-infectious model. Beyond the need to find different transmission parameters for each leaf, there are a few modifications to the standard SI model: a spatial aggregation parameter (accounting for the fact that plant cells are not subject to random mixing) and transmission from leaves further down the phloem.</p>
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| − | <p>The real strength of this work, however, is in the data. By using flow cytometry to measure the viral load of large numbers of plant cells, tracking over both time and space (although only at the scale of leaves). These experiments used Tobacco Etch Virus in nicotiana tabacum, and measured 50 000 cells per leaf for each sample, with 5 replicates. This allowed for the measurement of important parameters, such as the viral multiplicity of infection (which we know to be higher for CaMV (Gutierrez et al. 2010)) and the cellular contagion rate – the number of secondary infections per infected cell per day. The cellular contagion rate is an informative parameter, giving a detailed portrayal of the dynamics of the infection over time. In this case, it was found to be small: it decreased from an already-low value of 1.342 cells/cell/day 3 days post-infection down to 0.196 cells/cell/day 7 days post-infection. These low values may, however, be characteristic of plant RNA viruses (Tromas et al. 2014) -- meaning we might see something different for CaMV.</p>
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| − | <h3>Summary</h3>
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| − | <p>These two different modeling approaches both tackle the difficult issue of within-host virus spread. They are quite different, since each focuses on a different unit of spatial analysis. Neither incorporates mechanistic modeling of plant defenses – the first model only investigates up to the point of systemic infection (and includes no variable defenses against local spread), while the second model incorporates defense only indirectly via reductions in the cellular contagion rate (which could also be due to exhaustion of susceptibles, changing viral strategies, or other factors). We will have to pick our unit of spatial analysis based on the measurements we can take and the research questions we wish to pursue. It might be interesting to incorporate modeling of the plant adaptive immune response into the viral spread model – beyond simply being useful, this could be new research.</p>
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| − | <h3>Differences Compared to CaMV and Arabidopsis</h3>
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| − | <p>(This section is more a compilation of rough notes, but it's important to track these differences. These quotes are all direct from Tromas et al. 2014.)
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| − | “Moreover, this array of plant immune mechanisms probably contributes to the relatively low between-host variation typically found in experimental settings (Zwart et al. 2012)”.
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| − | “For Cauliflower mosaic virus (CaMV), MOI was reported to vary from 2 to 13 over time, and most cells were infected (Gutierrez 2010). Furthermore, for CaMV virion concentrations in vascular tissue are correlated to MOI (Gutierrez et al. 2012)”
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| − | “On the other hand, in a similar model-selection-based analysis for TMV and CaMV MOI, two viruses that also move by cell-to-cell movement, spatial aggregation only marginally improved model fit for both datasets (Zwart et al. 2013).”</p>
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