Enhanced Antigen Processing or Immune Evasion? West Nile Virus and the Induction of Immune Recognition Molecules

King, Nicholas J. C.; Davison, Ariane M; Getts, Daniel R.; Lu, David P; Getts, Meghann Teague; Yeung, Amanda W. S.; Peterson, James K.; Kesson, Alison

doi:10.1007/978-0-387-79840-0_14

Cited by 3 publications

(4 citation statements)

References 121 publications

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“…The increased avidity enables the interaction of infected cells with CTL clones previously below the recognition threshold because of their low affinity for MHC-virus peptide ligand. Furthermore, a polyclonal anti-viral CTL population can include self-reactive low-affinity clones that recognize MHC-I-self peptide configurations as discussed in [5]- [7]. The previous simulation results in [2] offered support for this point of view.…”

Section: Introductionmentioning

confidence: 84%

“…We divide this universe of cells into 3 N larger cells or coarse cubes for computational ease in our simulations with 512 N = and we let i C denote the collateral damage estimate in course cube i. We assume 7 p cells in a given coarse cube can upregulate due to the infection. In the simulation, the number of new infected cells that are not dividing and are removed in cube i is denoted by Next, assume this is true for all coarse cubes, giving 14 7 512 .…”

Section: T P Y T P Y T Y T Y Tmentioning

confidence: 99%

“…In flavivirus infection, however, the mortality associated with intervening doses is unpredictable over several log10 dilutions of virus dose. This results in a ragged dose-response curve depicting population mortality [7] whose data we show in Figure 1. The work in this paper builds on the model of the host-pathogen interactions for flaviviruses detailed in [2] and is used to …”

Section: Introductionmentioning

confidence: 99%

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Using a Collateral Damage Model to Explain Survival Data in West Nile Virus Infections

Peterson¹,

Kesson²,

King³

2016

AiM

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Simulation code for a model of the adaptive immune response seen in flavivirus infections is used to explain the immunopathological consequences seen in West Nile Virus virus (WNV) infections. We use a model that specifically handles the differences in how the virus infects resting cells, the G 0 state, versus dividing cells, the G 1 state, which includes vastly increased MHC-I upregulation for resting cells over dividing cells. The simulation suggests how the infection progresses in a one host model and the results shed insight into the unusual survival curve data obtained for this infection: there is an increase in health even though viral load has increased.

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Section: Introductionmentioning

confidence: 84%

Section: T P Y T P Y T Y T Y Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a Collateral Damage Model to Explain Survival Data in West Nile Virus Infections

Peterson¹,

Kesson²,

King³

2016

AiM

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“…Monocytes rapidly become TipDC (producing TNF and nitric oxide) in the DLN and likely contribute to the initiation of type 1 immune responses by inducing more IFN-γ from responding T cells than other DC subtypes in the DLN (Davison & King, 2011). Since flaviviruses significantly upregulate MHC expression on infected cells (King & Kesson, 1988;Shen et al, 1997), it is of interest how this influences the outcomes of innate immune interactions, such as with NK cells, and/or bears on the ultimate affinities of later adaptive cytotoxic T cell responses, reviewed in detail elsewhere Lobigs et al, 2003;King et al, 2009). At the same time, at the dermal site of infection, CCL2, produced by infected cells, also recruits considerable numbers of MDM, which surround the infected focus, becoming DC within 24h of arrival.…”

Section: Earliest Local Changes In Dermis and Draining Lymph Nodesmentioning

confidence: 99%

The Immunopathogenesis of Neurotropic Flavivirus Infection

King¹,

C²,

Rl³

et al. 2011

Flavivirus Encephalitis

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A Simulation for Flavivirus Infection Decoy Responses

Peterson

Kesson²,

King³

2015

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In this work, we discuss the development of simulation code for a model of the cross-reactive adaptive immune response seen in flavivirus infections. The model specifically addresses flavivirus pathogen virulence in G 0 vs. G 1 cell states. The MHC-I upregulation of resting cells (G 0 state) allows the T-cells generated for flavivirus peptide antigens to attack healthy cells also. The cells in G 1 state are not upregulated as much and so virus hides in them and hence is propagated upon rupture. Hence, this type of model is referred to as a decoy model because the immune system is decoyed into preferentially recognizing the upregulated cells while the virus actively propagates in another small, but important, cell population. We show that the generic assumption of upregulation via a model which includes the G G 0 1 differential upregulation leads to immunopathological consequences. We outline the details behind the simulation code decisions and provide some theoretical justification for our model of collateral damage and upregulation.

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Enhanced Antigen Processing or Immune Evasion? West Nile Virus and the Induction of Immune Recognition Molecules

Cited by 3 publications

References 121 publications

Using a Collateral Damage Model to Explain Survival Data in West Nile Virus Infections

Using a Collateral Damage Model to Explain Survival Data in West Nile Virus Infections

The Immunopathogenesis of Neurotropic Flavivirus Infection

A Simulation for Flavivirus Infection Decoy Responses

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