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Benjamin James
Benjamin James

LS Magazines Models (709) Mp4 EXCLUSIVE

Fitting of our model to in vitro data supported expected immune phenotypes for different bat cell lines as described in the literature. Simple target cell models that ignore the effects of immunity best recapitulated infectious time series derived from IFN-deficient Vero cells, while models assuming induced immune processes most accurately reproduced trials derived from RoNi/7.1 (Rousettus aegyptiacus) cells, which possess a standard virus-induced IFN-response. In most cases, models assuming constitutive immune processes best recreated virus epidemics produced on PaKiT01 (Pteropus alecto) cells, which are known to constitutively express the antiviral cytokine, IFN-α (Zhou et al., 2016). Model support for induced immune assumptions in fits to rVSV-MARV infections on PaKiT01 cells suggests that the constitutive IFN-α expression characteristic of P. alecto cells may represent more of a constitutive immune priming process than a perpetual, functional, antiviral defense. Results from mean field model fitting were additionally confirmed in spatially explicit stochastic simulations of each time series.

LS Magazines Models (709) mp4

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As previously demonstrated in within-host models for HIV (Coffin, 1995; Perelson et al., 1996; Nowak et al., 1995; Bonhoeffer et al., 1997; Ho et al., 1995), assumptions of simple target-cell depletion can often provide satisfactory approximations of viral dynamics, especially those reproduced in simple in vitro systems. Critically, our model fitting emphasizes the need for incorporation of top-down effects of immune control in order to accurately reproduce infectious time series derived from bat cell tissue cultures, especially those resulting from the robustly antiviral PaKiT01 P. alecto cell line. These findings indicate that enhanced IFN-mediated immune pathways in bat reservoirs may promote elevated within-host virus replication rates prior to cross-species emergence. We nonetheless acknowledge the limitations imposed by in vitro experiments in tissue culture, especially involving recombinant viruses and immortalized cell lines. Future work should extend these cell culture studies to include measurements of multiple state variables (i.e. antiviral cells) to enhance epidemiological inference.

This paper is an excellent example of how in vitro models of cell-virus interactions can be used to shape and formulate more general and larger-scale hypotheses about epidemiological dynamics. In this case, the choice of bat cell lines expressing induced and constitutive immune phenotypes enables estimates of different viral propagation rates. The results suggest that if bat cells do have greater constitutive immunity, this could lead to situations in which viruses that do propagate in bats will do so with much greater vigour (and possibly virulence and transmissability) should they 'spill-over' into non-bat hosts. The paper should be of wide general interest to those with interests in emerging disease dynamics and to quantitative biologists interested in the mathematical modelling of in vitro systems.

One reviewer indicates that if the paper has set out to answer the question as to whether the immune phenotype of bats alters the capacity for viruses to persist within them, the answers are not sufficiently clear to merit publication in eLife. This is an understandable point of view: it seems that none of the best-fitting well-mixed models are constitutive, and only one of the spatially explicit models is constitutive where we might expect it to be (and one where we wouldn't expect it to fit best). However, I have decided to go with the majority view and provide an opportunity for revision, but the authors should consider carefully whether the essential points below can be met (or convincingly rebutted).

1) Overall, the reviewers felt that this paper was quite a bit more complicated in the presentation of its results than it needs to be, and I encourage the authors to find ways of simplifying the Results section and linearizing the framing of the key messages. I wondered what the benefits of presenting both the well-mixed and spatial models were? Which is the most appropriate? Do they say importantly different things? Could the manuscript be simplified by focusing on just one?

2) Another key question the authors should consider is whether the data in Figure 4 really support (or even need) the cell turnover embedded within the formulation of the model? Could the main point of the paper be more clearly made by fitting simpler models more consistent with the short time frame of the experimental data, and concentrating on the initial infection spread and sometime declines, rather than a scenario requiring cell turnover? If the cell cultures were capable of maintaining infection over the longer term, why weren't the experiments run over longer time frames to demonstrate this? 041b061a72


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