Applications of omics approaches to the development of microbiological risk assessment using RNA virus dose-response models as a case study

Abstract

SummaryThe last decade has seen a huge increase in the amount of 'omics' data available and in our ability to interpret those data. The aim of this paper was to consider how omics techniques can be used to improve and refine microbiological risk assessment, using dose-response models for RNA viruses, with particular reference to norovirus through the oral route as the case study. The dose-response model for initial infection in the gastrointestinal tract is broken down into the component steps at the molecular level and the feasibility of assigning probabilities to each step assessed. The molecular mechanisms are not sufficiently well understood at present to enable quantitative estimation of probabilities on the basis of omics data. At present, the great strength of gene sequence data appears to be in giving information on the distribution and proportion of susceptible genotypes (for example due to the presence of the appropriate pathogen-binding receptor) in the host population rather than in predicting specificities from the amino acid sequences concurrently obtained. The nature of the mutant spectrum in RNA viruses greatly complicates the application of omics approaches to the development of mechanistic dose-response models and prevents prediction of risks of disease progression (given infection has occurred) at the level of the individual host. However, molecular markers in the host and virus may enable more broad predictions to be made about the consequences of exposure in a population. In an alternative approach, comparing the results of deep sequencing of RNA viruses in the faeces/vomitus from donor humans with those from their infected recipients may enable direct estimates of the average probability of infection per virion to be made.

Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus

“…Thus the more cells with bound virus then the greater the chance that infection will be successful in at least one of them. The probability p cell depends on ability of the bound virus to enter the cell, replicate and bud (Gale et al, 2014;Gale, 2017) and is not discussed further here. Now…”

“…The infection process of a host cell can be broken down into the component steps and modelled mathematically (Handel et al, 2014) and the probability of infection can be expressed as a function of the combined probabilities of each step (Gale et al, 2014). These steps include overcoming the initial host defenses, binding of the virion to its host cell receptor, entry to the host cell (i.e.…”

“…These steps include overcoming the initial host defenses, binding of the virion to its host cell receptor, entry to the host cell (i.e. internalisation and uncoating of the virion), and replication, capsid assembly and budding (Gale et al, 2014). Previously it was demonstrated that a dose-response model could, in part, be parameterized using thermodynamic data for some of the key molecular interactions in the infection process (Gale, 2017).…”

Quantitative Microbiological Risk Assessment and Source Attribution for Salmonella: Taking it Further

“…already comment that this will be the next step for source attribution but at the same time acknowledge that increasing the discriminatory power by applying WGS challenges the currently used Bayesian attribution models, meaning that new developments are needed. In the area of QMRA, Gale et al . suggest that omics approaches may provide valuable information for the modeling of dose‐response relationships.…”

Latest developments in foodborne pathogen risk assessment

“…However the potential metabolic capacities of pathogens to adapt to food and host environments would worthy to be investigated [37,38], that might be done by combining different omic strategies altogether as suggested by Yang [39]. On the other hand, Gale et al [40] explored the possibility of building a dose-response model for RNA virus but they could not succeed in having a quantitative estimation of probability of infection on the basis of omic data. Finally, metabolomics and more specifically fluxomics seems a valuable technique to progress on hazard characterization [41], even if reconstructing causal gene-metabolite network will be always difficult [42].…”