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. 2006 Aug 7;273(1596):1961-7.
doi: 10.1098/rspb.2006.3506.

Modelling cytomegalovirus replication patterns in the human host: factors important for pathogenesis

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Modelling cytomegalovirus replication patterns in the human host: factors important for pathogenesis

Roland R Regoes et al. Proc Biol Sci. .

Abstract

Human cytomegalovirus can cause a diverse range of diseases in different immunocompromised hosts. The pathogenic mechanisms underlying these diseases have not been fully elucidated, though the maximal viral load during infection is strongly correlated with the disease. However, concentrating on single viral load measures during infection ignores valuable information contained during the entire replication history up to the onset of disease. We use a statistical model that allows all viral load data sampled during infection to be analysed, and have applied it to four immunocompromised groups exhibiting five distinct cytomegalovirus-related diseases. The results show that for all diseases, peaks in viral load contribute less to disease progression than phases of low virus load with equal amount of viral turnover. The model accurately predicted the time of disease onset for fever, gastrointestinal disease and pneumonitis but not for hepatitis and retinitis, implying that other factors may be involved in the pathology of these diseases.

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Figures

Figure 1
Figure 1
Distribution of the peak parameter, p, illustrating the goodness-of-fit, measured by the fraction of explained variation in times to disease for four of the five HCMV diseases under consideration (fever—solid line; pneumonitis—dotted line, gastrointestinal disease—dashed line and hepatitis—dashed-dotted line). All fitted peak parameters are between 0 and 1. The fraction of explained variation in time to disease is significant for all diseases, except for hepatitis (n=4) (see table 1).
Figure 2
Figure 2
Agreement between the time of disease predicted by the model, tjexp, and the observed time to disease, tjdisease, for the three HCMV diseases where a significant proportion of the variation in time to disease was explained by the model. In the case of gastrointestinal disease, the significance of the regression is dependent on the outlying high data point.
Figure 3
Figure 3
Comparison of representative longitudinal HCMV load profiles measured in the blood of patients with fever, gastrointestinal disease and pneumonitis with the effective viral load ‘felt’ by the host (viral load scaled by the peak parameter, p). Dashed line, viral load; solid line, virus load ‘felt’ (v^p).

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