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Combining laboratory and mathematical models to infer mechanisms underlying kinetic changes in macrophage susceptibility to an RNA virus

Overview of attention for article published in BMC Systems Biology, October 2016
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3 tweeters

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5 Dimensions

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13 Mendeley
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Title
Combining laboratory and mathematical models to infer mechanisms underlying kinetic changes in macrophage susceptibility to an RNA virus
Published in
BMC Systems Biology, October 2016
DOI 10.1186/s12918-016-0345-5
Pubmed ID
Authors

Doeschl-Wilson, Andrea, Wilson, Alison, Nielsen, Jens, Nauwynck, Hans, Archibald, Alan, Ait-Ali, Tahar, Andrea Doeschl-Wilson, Alison Wilson, Jens Nielsen, Hans Nauwynck, Alan Archibald, Tahar Ait-Ali

Abstract

Macrophages are essential to innate immunity against many pathogens, but some pathogens also target macrophages as routes to infection. The Porcine Reproductive and Respiratory Syndrome virus (PRRSV) is an RNA virus that infects porcine alveolar macrophages (PAMs) causing devastating impact on global pig production. Identifying the cellular mechanisms that mediate PAM susceptibility to the virus is crucial for developing effective interventions. Previous evidence suggests that the scavenger receptor CD163 is essential for productive infection of PAMs with PRRSV. Here we use an integrative in-vitro-in-silico modelling approach to determine whether and how PAM susceptibility to PRRSV changes over time, to assess the role of CD163 expression on such changes, and to infer other potential causative mechanisms altering cell susceptibility. Our in-vitro experiment showed that PAM susceptibility to PRRSV changed considerably over incubation time. Moreover, an increasing proportion of PAMs apparently lacking CD163 were found susceptible to PRRSV at the later incubation stages, thus conflicting with current understanding that CD163 is essential for productive infection of PAMs with PRRSV. We developed process based dynamic mathematical models and fitted these to the data to assess alternative hypotheses regarding potential underlying mechanisms for the observed susceptibility and biomarker trends. The models informed by our data support the hypothesis that although CD163 may have enhanced cell susceptibility, it was not essential for productive infection in our study. Instead the models promote the existence of a reversible cellular state, such as macrophage polarization, mediated in a density dependent manner by autocrine factors, to be responsible for the observed kinetics in cell susceptibility. Our dynamic model-inference approach provides strong support that PAM susceptibility to the PRRS virus is transient, reversible and can be mediated by compounds produced by the target cells themselves, and that these can render PAMs lacking the CD163 receptor susceptible to PRRSV. The results have implications for the development of therapeutics aiming to boost target cell resistance and prompt future investigation of dynamic changes in macrophage susceptibility to PRRSV and other viruses.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 31%
Student > Bachelor 2 15%
Lecturer > Senior Lecturer 1 8%
Student > Doctoral Student 1 8%
Student > Ph. D. Student 1 8%
Other 3 23%
Unknown 1 8%
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 3 23%
Engineering 2 15%
Mathematics 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Computer Science 1 8%
Other 2 15%
Unknown 3 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 October 2017.
All research outputs
#7,514,901
of 12,016,495 outputs
Outputs from BMC Systems Biology
#564
of 998 outputs
Outputs of similar age
#142,574
of 253,580 outputs
Outputs of similar age from BMC Systems Biology
#11
of 17 outputs
Altmetric has tracked 12,016,495 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 998 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.