Viral infections are a major cause of infectious diseases worldwide. Inflammation and the immune system are the major host defenses against these viral infection. Prostaglandin E2 (PGE2), an eicosanoid generated by cyclooxygenases, has been shown to modulate inflammation and the immune system by regulating the expression/concentration of cytokines. The effect of PGE2 on viral infection and replication is cell type- and virus-family-dependent. The host immune system can be modulated by PGE2, with regards to immunosuppression, inhibition of nitrogen oxide (NO) production, inhibition of interferon (IFN) and apoptotic pathways and inhibition of viral receptor expression. Furthermore, PGE2 can play a role in viral infection directly by increasing the production and release of virions, inhibiting viral binding and replication, and/or stimulating viral gene expression. Prostaglandin E2 may also have a regulatory role in the induction of autoimmunity and in signaling via Toll-like receptors. In this review the known effects of PGE2 on the pathogenesis of various infections caused by herpes simplex virus, rotavirus, influenza A virus and human immunodeficiency virus as well the therapeutic potential of PGE2 are discussed.

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence – by controlling for phylogenetic structure – for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease. New strains of viruses arise continually. Consequently, predicting when past exposure to closely related strains will protect against infection by novel strains is central to understanding the dynamics of a broad range of the world's most important infectious diseases. While previous research has developed valuable tools for describing the observed antigenic landscapes, our ability to predict cross-protection between different viral strains depends almost entirely on cumbersome and expensive live animal work, often restricted to model species rather than the natural host. The development of computer-based approaches to the estimation of cross-protection from viral sequence data would be hugely valuable, and our study represents a significant step towards this research goal.

The results of a simple pairwise-scanning analysis designed to identify inter-serotype recombination fragments, applied to genome data from 156 isolates of Foot-and-mouth disease virus (FMDV) representing all seven serotypes, are reported. Large numbers of candidate recombinant fragments were identified from all parts of the FMDV genome, with the exception of the capsid genes, within which such fragments are infrequent. As expected, intertypic fragment exchange is most common between geographically sympatric FMDV serotypes. After accounting for the likelihood of intertypic convergence in highly conserved parts of the FMDV genome, it is concluded that intertypic recombination is probably widespread throughout the non-structural genes, but that recombination over the 2B/C and 3B/C gene boundaries appears to be less frequent than expected, given the large numbers of recombinant gene fragments arising in these genes.