Inhibition of infection spread by co-transmitted defective interfering particles

Baltes, Ashley; Akpinar, Fulya; İnankur, Bahar; Yin, John

doi:10.1371/journal.pone.0184029

Cited by 21 publications

(32 citation statements)

References 59 publications

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“…Interestingly, the speed of IPs spread at the front remained constant even when the front difference of DIPs and IPs was very small (see Figure 3(g), (h) and Figure 5(c), (d)). This provides a good contrast with a previous study which has demonstrated a considerable delay in the spread of infectious particles (Baltes et al, 2017),…”

Section: Discussioncontrasting

confidence: 83%

“…These particles have shown a substantial decrease in the virus growth without any direct interaction to the wild-type viruses, i.e. IPs, (Baltes et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…This provides an interesting comparison to a recent study in which IPs spread is substantially reduced with the presence of DIPs. Thus, the close proximity between the DIPs and IPs requires stochastic effects at the travelling front in order to bring delays in the virus spread (Baltes et al, 2017) which brings a challenge to be addressed by deterministic modeling.…”

Section: Dips Efficiency Depends On Maturation Rate Of Co-infected Cellsmentioning

confidence: 99%

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Report on the spatial spread of defective interfering particles and its role in suppressing infectious particles

Ali¹,

Ke²

2019

Preprint

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Defective interfering particles (DIPs) are categorized as non-infectious viruses with large deletions in their genomic material. A cell infected by a DIP require co-infection by a wild-type virus to complete its replicative lifecycle. There is an increasing interest in developing DIP based therapies in the form of molecular parasites that steal genetic resources of infectious particles. This parasitic behavior is enhanced by constructing engineering designs of DIPs to optimize their role in suppressing the virus infection within-host. Recent experimental studies characterize viral infection as a spatial process and emphasize on its spread rate and the area populated by the infectious particles (IPs). We developed a spatio-temporal model in the framework of reaction-diffusion equations to depict the functional organization of virus particles distributed over a tissue surface. Our model investigates the scenarios and figures out the aspects that can play a vital role to suppress the infection within-host. We studied the impact of initial dose of DIPs, the efficiency of DIP production and the role of cell maturation. Our results show that an engineered DIP can substantially decrease the concentration of IPs. We assert that the decrease in the rate of spatial spread of IPs requires non-deterministic settings.

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Section: Discussioncontrasting

confidence: 83%

“…These particles have shown a substantial decrease in the virus growth without any direct interaction to the wild-type viruses, i.e. IPs, (Baltes et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Section: Dips Efficiency Depends On Maturation Rate Of Co-infected Cellsmentioning

confidence: 99%

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Report on the spatial spread of defective interfering particles and its role in suppressing infectious particles

Ali¹,

Ke²

2019

Preprint

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“…If we cannot guarantee that a fast-growing virus will suppress growth of a slow-growing virus, then this strategy might be risky. For example, some have suggested using defective interfering particles (DIPs) as a possible method for blocking infections [51][52][53][54][55]. DIPs cannot replicate on their own, but have a high growth rate when fully-functioning virus is present.…”

Section: Discussionmentioning

confidence: 99%

Effect of stochasticity on coinfection dynamics of respiratory viruses

2019

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Background Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality. Results In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection. Conclusions We derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus. Electronic supplementary material The online version of this article (10.1186/s12859-019-2793-6) contains supplementary material, which is available to authorized users.

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“…5), and it is expected that also in DIP-virus system, there is a tendency to self-organize so that the 445 intact virus take over the front. It is worth mentioning that experiment on the spreading of DIPs and viruses mixture in space reported highly heterogeneous pattern in some conditions [43], demonstrating the importance of stochastic-ity. It is an interesting future work to extend the model to include stochastic population fluctuations in pirate-helper phage system, as well as analyzing it…”

mentioning

confidence: 97%

How pirate phage interferes with helper phage: Comparison of the two distinct strategies

Mitarai

2019

Preprint

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Pirate phages use the structural proteins encoded by unrelated helper phages to propagate. The best-studied example is the pirate P4 and helper P2 of coliphages, and it has been known that the Staphylococcus aureus pathogenicity islands (SaPIs) that can encode virulence factors act as pirate phages, too.When alone in the host, the pirate phages act as a prophage, but when the helper phage gene is also in the same host cell, the pirate phage has ability to exploit the helper phages structural proteins to produce pirate phage particles and spread, interfering with the helper phage production. The known helper phages in these systems are temperate phage. Interestingly, the interference of the pirate phage to the helper phage occurs in different manner between the SaPI-helper system and the P4-P2 system. The SaPIs cannot lyse the helper lysogen upon infection, while when the helper phage lyse the SaPI lysogen, most of the phage particles produced are SaPI particles. On the contrary, in the P4-P2 system, the pirate P4 can lyse helper P2 lysogens to produce mostly the P4 particles, while when P2 phage lyses a P4 lysogen, most of the produces phages are P2 particles. Here, the consequences of these different strategies in the pirate and helper phage spreading among uninfected host was analyzed by using mathematical model. Both in the well-mixed environment and the spatially structured environment, SaPI's strategy interfere with the helper phage * Corresponding author Email address: mitarai@nbi.dk (Namiko Mitarai) spreading significantly more than the P4's strategy, because SaPI interfere with the helper phage's main reproduction step, while P4 interfere only by forcing the helper lysogens to lyse. However, the interference is weaker in the spatially structured environment, because the system self-organize so that the helper phages take over the front of propagation due to the need of helper phage for the pirate phage spreading.

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Inhibition of infection spread by co-transmitted defective interfering particles

Cited by 21 publications

References 59 publications

Report on the spatial spread of defective interfering particles and its role in suppressing infectious particles

Report on the spatial spread of defective interfering particles and its role in suppressing infectious particles

Effect of stochasticity on coinfection dynamics of respiratory viruses

How pirate phage interferes with helper phage: Comparison of the two distinct strategies

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